Целевые каузальные эффекты в социальных исследованиях
Научная статья
Выражение признательности
Статья подготовлена в рамках Программы фундаментальных исследований Национального исследовательского университета «Высшая школа экономики». Автор выражает признательность за ценные советы и комментарии редактору и анонимному рецензенту, а также Алексею Бессуднову, Егору Лазареву, Елизавете Попковой и Максиму Рудневу и Юрию Рыкову.
Для цитирования
Соколов Б.О. Целевые каузальные эффекты в социальных исследованиях // Социология: методология, методы, математическое моделирование (Социология:4М). 2025. № 61. С. 7-76. DOI: https://doi.org/10.19181/4m.2025.34.2.1 EDN: VCXNGL
Аннотация
Данная работа представляет собой обзор основных целевых эффектов (эстимандов), используемых в современных научных и прикладных исследованиях для операционализации каузальных запросов в терминах модели Рубина. Сначала рассматриваются наиболее популярные средние эффекты воздействия (АTE, АTT, АTC). Затем описываются их расширения, в том числе разнообразные локальные и условные эффекты воздействия; целевые величины, квантифицирующие каузальные взаимодействия и каузальную медиацию; эффекты воздействия многозначных и интервальных переменных; а также динамические причинно-следственные связи. Для каждого из этих эстимандов даëтся содержательное объяснение и приводятся примеры практических вопросов, на которые они позволяют ответить. Также обсуждаются допущения, необходимые для непараметрической идентификации ряда эффектов, и возможные методы их оценки.
Ключевые слова:
статистический причинно-следственный вывод, эффект воздействия, каузальная модель Рубина, доказательная политика, эстиманд, количественные методы
Литература
1. Ениколопов Р. С. Доказательная экономика развития: Нобелевская премия по экономике 2019 года // Вопросы экономики. 2020, № 1. С. 5–17. DOI: 10.32609/0042-8736-2020-1-5-17. EDN: WXVLIN.
2. Серебренников Д. Е., Кузьмина Ю. В. Полевые эксперименты и модель причинно-следственного вывода Дональда Рубина: обзор актуальных исследований // Экономическая социология. 2021, т. 22, № 4. С. 117–139. DOI: 10.17323/1726-3247-2021-4-117-139. EDN: HQERRJ.
3. Heckman J. J. Econometric causality // International statistical review. 2008, vol. 76, № 1. P. 1–27. DOI: 10.1111/j.1751-5823.2007.00024.x.
4. Сонин К. И. Вместо лаборатории: анализ данных естественных экспериментов (Нобелевская премия по экономике 2021 года) // Вопросы экономики. 2022, № 1. С. 5–22. DOI: 10.32609/0042-8736-2022-1-5-22. EDN: MHOZVB.
5. Hitchcock C. Probabilistic causation // The Stanford encyclopedia of philosophy (Spring 2021 Edition) / Ed. By E. N. Zalta. 2021. URL: https://plato.stanford.edu/archives/spr2021/entries/causation-probabilistic/ (дата обращения: 05.10.2025).
6. Imbens G. W. Causal inference in the social sciences // Annual review of statistics and its application. 2024, vol. 11. P. 123–152. DOI: 10.1146/annurev-statistics-033121-114601.
7. Hassell H. J., Holbein J. B. Navigating potential pitfalls in difference-in-differences designs: Reconciling conflicting findings on mass shootings' effect on electoral outcomes // American political science review. 2024, vol. 119, № 1. P. 240–260. DOI: 10.1017/S0003055424000108.
8. Greifer N., Stuart E. A. Choosing the estimand when matching or weighting in observational studies. arXiv:2106.10577 [Stat], June 2021. DOI: 10.48550/arXiv.2106.10577.
9. Heiss A. Demystifying causal inference estimands: ATE, ATT, and ATU // Andrew Heiss: [сайт]. 21.03.2024. DOI: 10.59350/c9z3a-rcq16. URL: https://www.andrewheiss.com/blog/2024/03/21/demystifying-ate-att-atu/ (дата обращения: 05.10.2025).
10. Barrett M., D'Agostino McGowan L., Gerke T. Causal inference in R // R-Causal: [сайт]. 21.08.2025. URL: https://www.r-causal.org/ (дата обращения: 05.10.2025).
11. Lundberg I., Johnson R., Stewart B. M. What is your estimand? Defining the target quantity connects statistical evidence to theory // American sociological review. 2021, vol. 86, № 3. P. 532–565. DOI: 10.1177/00031224211004187.
12. Neyman (Splawa-Neyman) J. On the application of probability theory to agricultural experiments. Essay on principles. Section 9 // Roczniki Nauk Rolniczych Tom X. 1923 [in Polish]; reprinted in Statistical science. 1990, vol. 5, № 4. P. 465–472 / Transl. by D. M. Dabrowska, T. P. Speed. URL: https://www.jstor.org/stable/2245382 (дата обращения: 05.10.2025).
13. Holland P. W. Statistics and causal inference // Journal of the American statistical association. 1986, vol. 81, № 396. P. 945–960. DOI: 10.1080/01621459.1986.10478354.
14. Rubin D. B. [On the application of probability theory to agricultural experiments. Essay on principles. Section 9.] Comment: Neyman (1923) and causal inference in experiments and observational studies // Statistical science. 1990, vol. 5, № 4. P. 472–480. URL: https://www.jstor.org/stable/2245383 (дата обращения: 05.10.2025).
15. Angrist J. D., Imbens G. W., Rubin D. B. Identification of causal effects using instrumental variables: Rejoinder // Journal of the American statistical association. 1996, vol. 91, № 434. P. 468–472. DOI: 10.2307/2291634.
16. Rubin D. B. Causal inference using potential outcomes: Design, modeling, decisions // Journal of the American statistical association. 2005, vol. 100, № 469. P. 322–331. DOI: 10.1198/016214504000001880.
17. Heckman J. J. Identification of causal effects using instrumental variables: Comment // Journal of the American statistical association. 1996, vol. 91, № 434. P. 459–462. DOI: 10.2307/2291631.
18. Pearl J. Causal inference: History, perspectives, adventures, and unification (An interview with Judea Pearl) // Observational studies. 2022, vol. 8, № 2. P. 23–36. DOI: 10.1353/obs.2022.0007.
19. Rubin D. B. Matching to remove bias in observational studies // Biometrics. 1973, vol. 29, № 1. P. 159–183. DOI: 10.2307/2529684.
20. Rubin D. B. The use of matched sampling and regression adjustment to remove bias in observational studies // Biometrics. 1973, vol. 29, № 1. P. 185–203. DOI: 10.2307/2529685.
21. Rubin D. B. Estimating causal effects of treatments in randomized and nonrandomized studies // Journal of educational psychology. 1974, vol. 66, № 5. P. 688–701. DOI: 10.1037/h0037350.
22. Rubin D. B. Assignment to treatment group on the basis of a covariate // Journal of educational and behavioral statistics. 1977, vol. 2, № 1. P. 1–26. DOI: 10.3102/10769986002001001.
23. Rubin D. B. Bayesian inference for causal effects: The role of randomization // The annals of statistics. 1978, vol. 6, № 1. P. 34–58. URL: https://www.jstor.org/stable/2958688 (дата обращения: 05.10.2025).
24. Pearl J. Causal diagrams for empirical research // Biometrika. 1995, vol. 82, № 4. P. 669–688. DOI: 10.1093/biomet/82.4.669.
25. Pearl J. Causality. 2nd ed. Cambridge: Cambridge University Press, 2009. 464 p. ISBN 978-0-521-89560-6. DOI: 10.1017/CBO9780511803161.
26. Bollen K. A., Pearl J. Eight myths about causality and structural equation models // Handbook of causal analysis for social research / Ed. by S. L. Morgan. Dordrecht: Springer Netherlands, 2013. P. 301–328. DOI: 10.1007/978-94-007-6094-3_15.
27. Markus K. A. Causal effects and counterfactual conditionals: Contrasting Rubin, Lewis and Pearl // Economics & philosophy. 2021, vol. 37, № 3. P. 441–461. DOI: 10.1017/S0266267120000437.
28. Imbens G. W. Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics // Journal of economic literature. 2020, vol. 58, № 4. P. 1129–1179. DOI: 10.1257/jel.20191597.
29. Shpitser I., Pearl J. Effects of treatment on the treated: Identification and generalization // Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009. Arlington: AUAI Press, 2009. P. 514–521. DOI: 10.5555/1795114.1795174.
30. Pearl J., Glymour M., Jewell N. P. Causal inference in statistics: A primer. UK: John Wiley & Sons, 2016. 160 p. ISBN 978-1-119-18686-1.
31. Cunningham S. Causal inference: The mixtape. New Haven: Yale University Press, 2021. 328 p. ISBN 978-0-300-25168-5.
32. Hernán M., Robins J. M. Causal inference: What if. Boca Raton: Chapman & Hall/CRC, 2020. 350 p. ISBN 978-1-420-07616-5.
33. Huntington-Klein N. The effect: An introduction to research design and causality. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2025. 686 p. ISBN 978-1-032-12578-7.
34. Ениколопов Р. С. Оценивание эффектов воздействия // Квантиль. 2009, № 6. С. 3–14.
35. Ньюи У. Эффекты воздействия // Квантиль. 2009. № 6. С. 15–23.
36. Седашов Е. А. Методы каузального анализа в современной политической науке // Политическая наука. 2021, № 1. С. 98–115. DOI: 10.31249/poln/2021.01.04. EDN: SWFGOG.
37. Де Мескита И.Б., Фаулер Э. Статистика без подвоха: методы критического анализа данных и причинного вывода / Пер. с англ. В. С. Яценкова. М.: ДМК Пресс, 2023.453 с. ISBN 978-5-93700-240-2.
38. Imbens G. W., Rubin D. B. Causal inference in statistics, social, and biomedical sciences. Cambridge: Cambridge University Press, 2015. 625 p. ISBN 978-0-521-88588-1. DOI: 10.1017/CBO9781139025751.
39. Imbens G. W., Xu Y. LaLonde (1986) after nearly four decades: Lessons learned. arXiv:2406.00827, 2024. DOI: 10.48550/arXiv.2406.00827.
40. Smith G. C., Pell J. P. Parachute use to prevent death and major trauma related to gravitational challenge: Systematic review of randomised controlled trials // BMJ. 2003, vol. 327, № 7429. P. 1459–1461. DOI: 10.1136/bmj.327.7429.1459.
41. Yeh R. W., Valsdottir L. R., Yeh M. W., Shen C., Kramer D. B., Strom J. B., Secemsky E. A., Healy J. L., Domeier R. M., Kazi D. S., Nallamothu B. K. Parachute use to prevent death and major trauma when jumping from aircraft: Randomized controlled trial // BMJ. 2018, vol. 363. k5094. DOI: 10.1136/bmj.k5094.
42. Rubin D. B. Comment: Which ifs have causal answers // Journal of the American statistical association. 1986, vol. 81, № 396. P. 961–962. DOI: 10.1080/01621459.1986.10478355.
43. Cole S. R., Frangakis C. E. The consistency statement in causal inference: A definition or an assumption? // Epidemiology. 2009, vol. 20, № 1. P. 3–5. DOI: 10.1097/EDE.0b013e31818ef366.
44. VanderWeele T. J. Concerning the consistency assumption in causal inference // Epidemiology. 2009, vol. 20, № 6. P. 880–883. DOI: 10.1097/ede.0b013e3181bd5638.
45. Pearl J. On the consistency rule in causal inference: Axiom, definition, assumption, or theorem? // Epidemiology. 2010, vol. 21, № 6. P. 872–875. DOI: 10.1097/ede.0b013e3181f5d3fd.
46. Naimi A. I., Whitcomb B. W. Defining and identifying average treatment effects // American journal of epidemiology. 2023, vol. 192, № 5. P. 685–687. DOI: 10.1093/aje/kwad012.
47. VanderWeele T. J., Ding P. Sensitivity analysis in observational research: Introducing the E-value // Annals of internal medicine. 2017, vol. 167, № 4. P. 268–274. DOI: 10.7326/M16-2607.
48. Cinelli C., Hazlett C. Making sense of sensitivity: Extending omitted variable bias // Journal of the Royal Statistical Society. Series B. 2020, vol. 82, № 1. P. 39–67. DOI: 10.1111/rssb.12348.
49. Rosenbaum P. R. Modern algorithms for matching in observational studies // Annual review of statistics and its application. 2020, vol. 7. P. 143–176. DOI: 10.1146/annurev-statistics-031219-041058.
50. Stolley P. D. When genius errs: R.A. Fisher and the lung cancer controversy // American journal of epidemiology. 1991, vol. 133, № 5. P. 416–425. DOI: 10.1093/oxfordjournals.aje.a115906.
51. Cornfield J., Haenszel W., Hammond E. C., Lilienfeld A. M., Shimkin M. B., Wynder E. L. Smoking and lung cancer: Recent evidence and a discussion of some questions // Journal of the National Cancer Institute. 1959, vol. 22, № 1. P. 173–203. DOI: 10.1093/jnci/22.1.173.
52. Ding P. A first course in causal inference. Chapman and Hall/CRC, 2024. 464 p. ISBN 978-1-032-75862-6.
53. Greifer N., Stuart E. A. Matching methods for confounder adjustment: An addition to the epidemiologist's toolbox // Epidemiologic reviews. 2021, vol. 43, № 1. P. 118–129. DOI: 10.1093/epirev/mxab003.
54. King G., Zeng L. The dangers of extreme counterfactuals // Political analysis. 2006, vol. 14, № 2. P. 131–159. DOI: 10.1093/pan/mpj004.
55. King G., Lucas C., Nielsen R. A. The balance‐sample size frontier in matching methods for causal inference // American journal of political science. 2017, vol. 61, № 2. P. 473–489. DOI: 10.1111/ajps.12272.
56. Greifer N. Assessing balance // Cran.R Project: [сайт]. 29.05.2025. URL: https://cran.r-project.org/web/packages/MatchIt/vignettes/assessing-balance.html (дата обращения: 05.10.2025).
57. Angrist J. D., Pischke J. S. Mostly harmless econometrics: An empiricist's companion. Princeton: Princeton University Press, 2009. 274 p. ISBN 978-0-691-12035-5. DOI: 10.1515/9781400829828.
58. Angrist J. D., Imbens G. W., Rubin D. B. Identification of causal effects using instrumental variables // Journal of the American statistical association. 1996, vol. 91, № 434. P. 444–455. DOI: 10.2307/2291629.
59. Cattaneo M. D., Idrobo N., Titiunik R. A. Practical introduction to regression discontinuity designs: Foundations. Cambridge: Cambridge University Press, 2020. 120 p. ISBN 978-1-108-71020-6. DOI: 10.1017/9781108684606.
60. Кузьмина Ю. В. Метод разрывной регрессии и метод отбора подобного по вероятности для оценки эффекта одного года обучения: опыт применения на примере данных PISA 2009 // Социология: методология, методы, математическое моделирование. 2014, № 38. С. 7–37. EDN: SZSTOZ.
61. Abadie A. Semiparametric instrumental variable estimation of treatment response models // Journal of econometrics. 2003, vol. 113, № 2. P. 231–263. DOI: 10.1016/S0304-4076(02)00201-4.
62. Angrist J. D., Imbens G. W. Two-stage least squares estimation of average causal effects in models with variable treatment intensity // Journal of the American statistical association. 1995, vol. 90, № 430. P. 431–442. DOI: 10.1080/01621459.1995.10476535.
63. Angrist J. D., Krueger A. B. Empirical strategies in labor economics // Handbook of labor economics. 1999, vol. 3. P. 1277–1366. DOI: 10.1016/S1573-4463(99)03004-7.
64. Cattaneo M. D., Titiunik R. A. Regression discontinuity designs // Annual review of economics. 2022, vol. 14, № 1. P. 821–851. DOI: 10.1146/annurev-economics-051520-021409.
65. Cattaneo M. D., Idrobo N., Titiunik R. A. Practical introduction to regression discontinuity designs: Extensions. Cambridge: Cambridge University Press, 2024. 122 p. ISBN 978-1-009-46232-7. DOI: 10.1017/9781009441896.
66. Freedman B. Equipoise and the ethics of clinical research // The New England journal of medicine. 1987, vol. 317, № 3. P. 141–145. DOI: 10.1056/nejm198707163170304.
67. Liu J., Liu Z., Xu Y. A practical guide to estimating conditional marginal effects: Modern approaches. arXiv, 2025. 2504.01355v1. DOI: 10.48550/arXiv.2504.01355.
68. Wager S., Athey S. Estimation and inference of heterogeneous treatment effects using random forests // Journal of the American statistical association. 2018, vol. 113, № 523. P. 1228–1242. DOI: 10.1080/01621459.2017.1319839.
69. Brand J. E., Zhou X., Xie Y. Recent developments in causal inference and machine learning // Annual review of sociology. 2023, vol. 49. P. 81–110. DOI: 10.1146/annurev-soc-030420-015345.
70. Bansak K., Hainmueller J., Hopkins D. J., Yamamoto T. Conjoint survey experiments // Advances in experimental political science / Eds. by J. N. Druckman, D. P. Green. Cambridge: Cambridge University Press, 2021. P. 19–41. DOI: 10.1017/9781108777919.004.
71. VanderWeele T. J. Explanation in causal inference: Methods for mediation and interaction. New York: Oxford University Press, 2015. 728 p. ISBN 978-0-19-932587-0.
72. Bertrand M., Mullainathan S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination // American economic review. 2004, vol. 94, № 4. P. 991–1013. DOI: 10.1257/0002828042002561.
73. Bessudnov A., Shcherbak A. Ethnic discrimination in multi-ethnic societies: Evidence from Russia // European sociological review. 2020, vol. 36, № 1. P. 104–120. DOI: 10.1093/esr/jcz045.
74. Egami N., Imai K. Causal interaction in factorial experiments: Application to conjoint analysis // Journal of the American statistical association. 2019, vol. 114, № 526. P. 529–540. DOI: 10.1080/01621459.2018.1476246.
75. Imai K., Keele L., Tingley D. A general approach to causal mediation analysis // Psychological methods. 2010, vol. 15, № 4. P. 309–334. DOI: 10.1037/a0020761.
76. Tingley D., Yamamoto T., Hirose K., Keele L., Imai K. Mediation: R package for causal mediation analysis // Journal of statistical software. 2014, vol. 59, № 5. P. 1–38. DOI: 10.18637/jss.v059.i05.
77. Bellani L., Bia M. The long-run effect of childhood poverty and the mediating role of education // Journal of the Royal Statistical Society series A: Statistics in society. 2019, vol. 182, № 1. P. 37–68. DOI: 10.1111/rssa.12388.
78. Daniel R., Zhang J., Farewell D. Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets // Biometrical journal. 2021, vol. 63, № 3. P. 528–557. DOI: 10.1002/bimj.201900297.
79. Morris T. P., Walker A. S., Williamson E. J., White I. R. Planning a method for covariate adjustment in individually randomised trials: A practical guide // Trials. 2022, vol. 23, № 1. 328. DOI: 10.1186/s13063-022-06097-z.
80. Greenland S. Noncollapsibility, confounding, and sparse-data bias. Part 1: The oddities of odds // Journal of clinical epidemiology. 2021, vol. 138. P. 178–181. DOI: 10.1016/j.jclinepi.2021.06.007.
81. Greifer N. Estimating effects after matching // Cran.R Project: [сайт]. 29.05.2025. URL: https://cran.r-project.org/web/packages/MatchIt/vignettes/estimating-effects.html (дата обращения: 05.10.2025).
82. Chernozhukov V., Hansen C. An IV model of quantile treatment effects // Econometrica. 2005, vol. 73, № 1. P. 245–261. DOI: 10.1111/j.1468-0262.2005.00570.x.
83. Abbring J. H., Heckman J. J. Econometric evaluation of social programs, part III: Distributional treatment effects, dynamic treatment effects, dynamic discrete choice, and general equilibrium policy evaluation // Handbook of econometrics. 2007, vol. 6. P. 5145–5303. DOI: 10.1016/S1573-4412(07)06072-2.
84. Lu J., Ding P., Dasgupta T. Treatment effects on ordinal outcomes: Causal estimands and sharp bounds // Journal of educational and behavioral statistics. 2018, vol. 43, № 5. P. 540–567. DOI: 10.3102/1076998618776435.
85. Di Francesco R., Mellace G. Causal inference for qualitative outcomes. arXiv, 2025. 2502.11691. DOI: 10.48550/arXiv.2502.11691.
86. Callaway B., Goodman-Bacon A., Sant'Anna P. H. Difference-in-differences with a continuous treatment. National Bureau of Economic Research, 2024. 59 p. w32117. DOI: 10.3386/w32117.
87. Imbens G. W. The role of the propensity score in estimating dose-response functions // Biometrika. 2000, vol. 87, № 3. P. 706–710. DOI: 10.1093/biomet/87.3.706.
88. Imai K., van Dyk D. A. Causal inference with general treatment regimes: Generalizing the propensity score // Journal of the American statistical association. 2004, vol. 99, № 467. P. 854–866. DOI: 10.1198/016214504000001187.
89. Вулдридж Д. М. Оценивание методом «разность разностей» // Квантиль. 2009, № 6. С. 15–23.
90. Baker A. C., Larcker D. F., Wang C. C. How much should we trust staggered difference-in-differences estimates? // Journal of financial economics. 2022, vol. 144, № 2. P. 370–395. DOI: 10.1016/j.jfineco.2022.01.004.
91. de Chaisemartin C., d'Haultfoeuille X. Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: A survey // The econometrics journal. 2023, vol. 26, № 3. P. 1–30. DOI: 10.1093/ectj/utac017.
92. Roth J., Sant'Anna P. H., Bilinski A., Poe J. What's trending in difference-in-differences? A synthesis of the recent econometrics literature // Journal of econometrics. 2023, vol. 235, № 2. P. 2218–2244. DOI: 10.1016/j.jeconom.2023.03.008.
93. Xu Y. Causal inference with time-series cross-sectional data: A reflection // Oxford handbook of engaged methodological pluralism in political science (online edition) / Eds. by J. M. Box-Steffensmeier, D. P. Christenson, V. Sinclair-Chapman. 2023. 34 p. DOI: 10.1093/oxfordhb/9780192868282.013.30.
94. Liu L., Wang Y., Xu Y. A practical guide to counterfactual estimators for causal inference with time‐series cross‐sectional data // American journal of political science. 2024, vol. 68, № 1. P. 160–176. DOI: 10.1111/ajps.12723.
2. Серебренников Д. Е., Кузьмина Ю. В. Полевые эксперименты и модель причинно-следственного вывода Дональда Рубина: обзор актуальных исследований // Экономическая социология. 2021, т. 22, № 4. С. 117–139. DOI: 10.17323/1726-3247-2021-4-117-139. EDN: HQERRJ.
3. Heckman J. J. Econometric causality // International statistical review. 2008, vol. 76, № 1. P. 1–27. DOI: 10.1111/j.1751-5823.2007.00024.x.
4. Сонин К. И. Вместо лаборатории: анализ данных естественных экспериментов (Нобелевская премия по экономике 2021 года) // Вопросы экономики. 2022, № 1. С. 5–22. DOI: 10.32609/0042-8736-2022-1-5-22. EDN: MHOZVB.
5. Hitchcock C. Probabilistic causation // The Stanford encyclopedia of philosophy (Spring 2021 Edition) / Ed. By E. N. Zalta. 2021. URL: https://plato.stanford.edu/archives/spr2021/entries/causation-probabilistic/ (дата обращения: 05.10.2025).
6. Imbens G. W. Causal inference in the social sciences // Annual review of statistics and its application. 2024, vol. 11. P. 123–152. DOI: 10.1146/annurev-statistics-033121-114601.
7. Hassell H. J., Holbein J. B. Navigating potential pitfalls in difference-in-differences designs: Reconciling conflicting findings on mass shootings' effect on electoral outcomes // American political science review. 2024, vol. 119, № 1. P. 240–260. DOI: 10.1017/S0003055424000108.
8. Greifer N., Stuart E. A. Choosing the estimand when matching or weighting in observational studies. arXiv:2106.10577 [Stat], June 2021. DOI: 10.48550/arXiv.2106.10577.
9. Heiss A. Demystifying causal inference estimands: ATE, ATT, and ATU // Andrew Heiss: [сайт]. 21.03.2024. DOI: 10.59350/c9z3a-rcq16. URL: https://www.andrewheiss.com/blog/2024/03/21/demystifying-ate-att-atu/ (дата обращения: 05.10.2025).
10. Barrett M., D'Agostino McGowan L., Gerke T. Causal inference in R // R-Causal: [сайт]. 21.08.2025. URL: https://www.r-causal.org/ (дата обращения: 05.10.2025).
11. Lundberg I., Johnson R., Stewart B. M. What is your estimand? Defining the target quantity connects statistical evidence to theory // American sociological review. 2021, vol. 86, № 3. P. 532–565. DOI: 10.1177/00031224211004187.
12. Neyman (Splawa-Neyman) J. On the application of probability theory to agricultural experiments. Essay on principles. Section 9 // Roczniki Nauk Rolniczych Tom X. 1923 [in Polish]; reprinted in Statistical science. 1990, vol. 5, № 4. P. 465–472 / Transl. by D. M. Dabrowska, T. P. Speed. URL: https://www.jstor.org/stable/2245382 (дата обращения: 05.10.2025).
13. Holland P. W. Statistics and causal inference // Journal of the American statistical association. 1986, vol. 81, № 396. P. 945–960. DOI: 10.1080/01621459.1986.10478354.
14. Rubin D. B. [On the application of probability theory to agricultural experiments. Essay on principles. Section 9.] Comment: Neyman (1923) and causal inference in experiments and observational studies // Statistical science. 1990, vol. 5, № 4. P. 472–480. URL: https://www.jstor.org/stable/2245383 (дата обращения: 05.10.2025).
15. Angrist J. D., Imbens G. W., Rubin D. B. Identification of causal effects using instrumental variables: Rejoinder // Journal of the American statistical association. 1996, vol. 91, № 434. P. 468–472. DOI: 10.2307/2291634.
16. Rubin D. B. Causal inference using potential outcomes: Design, modeling, decisions // Journal of the American statistical association. 2005, vol. 100, № 469. P. 322–331. DOI: 10.1198/016214504000001880.
17. Heckman J. J. Identification of causal effects using instrumental variables: Comment // Journal of the American statistical association. 1996, vol. 91, № 434. P. 459–462. DOI: 10.2307/2291631.
18. Pearl J. Causal inference: History, perspectives, adventures, and unification (An interview with Judea Pearl) // Observational studies. 2022, vol. 8, № 2. P. 23–36. DOI: 10.1353/obs.2022.0007.
19. Rubin D. B. Matching to remove bias in observational studies // Biometrics. 1973, vol. 29, № 1. P. 159–183. DOI: 10.2307/2529684.
20. Rubin D. B. The use of matched sampling and regression adjustment to remove bias in observational studies // Biometrics. 1973, vol. 29, № 1. P. 185–203. DOI: 10.2307/2529685.
21. Rubin D. B. Estimating causal effects of treatments in randomized and nonrandomized studies // Journal of educational psychology. 1974, vol. 66, № 5. P. 688–701. DOI: 10.1037/h0037350.
22. Rubin D. B. Assignment to treatment group on the basis of a covariate // Journal of educational and behavioral statistics. 1977, vol. 2, № 1. P. 1–26. DOI: 10.3102/10769986002001001.
23. Rubin D. B. Bayesian inference for causal effects: The role of randomization // The annals of statistics. 1978, vol. 6, № 1. P. 34–58. URL: https://www.jstor.org/stable/2958688 (дата обращения: 05.10.2025).
24. Pearl J. Causal diagrams for empirical research // Biometrika. 1995, vol. 82, № 4. P. 669–688. DOI: 10.1093/biomet/82.4.669.
25. Pearl J. Causality. 2nd ed. Cambridge: Cambridge University Press, 2009. 464 p. ISBN 978-0-521-89560-6. DOI: 10.1017/CBO9780511803161.
26. Bollen K. A., Pearl J. Eight myths about causality and structural equation models // Handbook of causal analysis for social research / Ed. by S. L. Morgan. Dordrecht: Springer Netherlands, 2013. P. 301–328. DOI: 10.1007/978-94-007-6094-3_15.
27. Markus K. A. Causal effects and counterfactual conditionals: Contrasting Rubin, Lewis and Pearl // Economics & philosophy. 2021, vol. 37, № 3. P. 441–461. DOI: 10.1017/S0266267120000437.
28. Imbens G. W. Potential outcome and directed acyclic graph approaches to causality: Relevance for empirical practice in economics // Journal of economic literature. 2020, vol. 58, № 4. P. 1129–1179. DOI: 10.1257/jel.20191597.
29. Shpitser I., Pearl J. Effects of treatment on the treated: Identification and generalization // Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, UAI 2009. Arlington: AUAI Press, 2009. P. 514–521. DOI: 10.5555/1795114.1795174.
30. Pearl J., Glymour M., Jewell N. P. Causal inference in statistics: A primer. UK: John Wiley & Sons, 2016. 160 p. ISBN 978-1-119-18686-1.
31. Cunningham S. Causal inference: The mixtape. New Haven: Yale University Press, 2021. 328 p. ISBN 978-0-300-25168-5.
32. Hernán M., Robins J. M. Causal inference: What if. Boca Raton: Chapman & Hall/CRC, 2020. 350 p. ISBN 978-1-420-07616-5.
33. Huntington-Klein N. The effect: An introduction to research design and causality. 2nd ed. Boca Raton: Chapman and Hall/CRC, 2025. 686 p. ISBN 978-1-032-12578-7.
34. Ениколопов Р. С. Оценивание эффектов воздействия // Квантиль. 2009, № 6. С. 3–14.
35. Ньюи У. Эффекты воздействия // Квантиль. 2009. № 6. С. 15–23.
36. Седашов Е. А. Методы каузального анализа в современной политической науке // Политическая наука. 2021, № 1. С. 98–115. DOI: 10.31249/poln/2021.01.04. EDN: SWFGOG.
37. Де Мескита И.Б., Фаулер Э. Статистика без подвоха: методы критического анализа данных и причинного вывода / Пер. с англ. В. С. Яценкова. М.: ДМК Пресс, 2023.453 с. ISBN 978-5-93700-240-2.
38. Imbens G. W., Rubin D. B. Causal inference in statistics, social, and biomedical sciences. Cambridge: Cambridge University Press, 2015. 625 p. ISBN 978-0-521-88588-1. DOI: 10.1017/CBO9781139025751.
39. Imbens G. W., Xu Y. LaLonde (1986) after nearly four decades: Lessons learned. arXiv:2406.00827, 2024. DOI: 10.48550/arXiv.2406.00827.
40. Smith G. C., Pell J. P. Parachute use to prevent death and major trauma related to gravitational challenge: Systematic review of randomised controlled trials // BMJ. 2003, vol. 327, № 7429. P. 1459–1461. DOI: 10.1136/bmj.327.7429.1459.
41. Yeh R. W., Valsdottir L. R., Yeh M. W., Shen C., Kramer D. B., Strom J. B., Secemsky E. A., Healy J. L., Domeier R. M., Kazi D. S., Nallamothu B. K. Parachute use to prevent death and major trauma when jumping from aircraft: Randomized controlled trial // BMJ. 2018, vol. 363. k5094. DOI: 10.1136/bmj.k5094.
42. Rubin D. B. Comment: Which ifs have causal answers // Journal of the American statistical association. 1986, vol. 81, № 396. P. 961–962. DOI: 10.1080/01621459.1986.10478355.
43. Cole S. R., Frangakis C. E. The consistency statement in causal inference: A definition or an assumption? // Epidemiology. 2009, vol. 20, № 1. P. 3–5. DOI: 10.1097/EDE.0b013e31818ef366.
44. VanderWeele T. J. Concerning the consistency assumption in causal inference // Epidemiology. 2009, vol. 20, № 6. P. 880–883. DOI: 10.1097/ede.0b013e3181bd5638.
45. Pearl J. On the consistency rule in causal inference: Axiom, definition, assumption, or theorem? // Epidemiology. 2010, vol. 21, № 6. P. 872–875. DOI: 10.1097/ede.0b013e3181f5d3fd.
46. Naimi A. I., Whitcomb B. W. Defining and identifying average treatment effects // American journal of epidemiology. 2023, vol. 192, № 5. P. 685–687. DOI: 10.1093/aje/kwad012.
47. VanderWeele T. J., Ding P. Sensitivity analysis in observational research: Introducing the E-value // Annals of internal medicine. 2017, vol. 167, № 4. P. 268–274. DOI: 10.7326/M16-2607.
48. Cinelli C., Hazlett C. Making sense of sensitivity: Extending omitted variable bias // Journal of the Royal Statistical Society. Series B. 2020, vol. 82, № 1. P. 39–67. DOI: 10.1111/rssb.12348.
49. Rosenbaum P. R. Modern algorithms for matching in observational studies // Annual review of statistics and its application. 2020, vol. 7. P. 143–176. DOI: 10.1146/annurev-statistics-031219-041058.
50. Stolley P. D. When genius errs: R.A. Fisher and the lung cancer controversy // American journal of epidemiology. 1991, vol. 133, № 5. P. 416–425. DOI: 10.1093/oxfordjournals.aje.a115906.
51. Cornfield J., Haenszel W., Hammond E. C., Lilienfeld A. M., Shimkin M. B., Wynder E. L. Smoking and lung cancer: Recent evidence and a discussion of some questions // Journal of the National Cancer Institute. 1959, vol. 22, № 1. P. 173–203. DOI: 10.1093/jnci/22.1.173.
52. Ding P. A first course in causal inference. Chapman and Hall/CRC, 2024. 464 p. ISBN 978-1-032-75862-6.
53. Greifer N., Stuart E. A. Matching methods for confounder adjustment: An addition to the epidemiologist's toolbox // Epidemiologic reviews. 2021, vol. 43, № 1. P. 118–129. DOI: 10.1093/epirev/mxab003.
54. King G., Zeng L. The dangers of extreme counterfactuals // Political analysis. 2006, vol. 14, № 2. P. 131–159. DOI: 10.1093/pan/mpj004.
55. King G., Lucas C., Nielsen R. A. The balance‐sample size frontier in matching methods for causal inference // American journal of political science. 2017, vol. 61, № 2. P. 473–489. DOI: 10.1111/ajps.12272.
56. Greifer N. Assessing balance // Cran.R Project: [сайт]. 29.05.2025. URL: https://cran.r-project.org/web/packages/MatchIt/vignettes/assessing-balance.html (дата обращения: 05.10.2025).
57. Angrist J. D., Pischke J. S. Mostly harmless econometrics: An empiricist's companion. Princeton: Princeton University Press, 2009. 274 p. ISBN 978-0-691-12035-5. DOI: 10.1515/9781400829828.
58. Angrist J. D., Imbens G. W., Rubin D. B. Identification of causal effects using instrumental variables // Journal of the American statistical association. 1996, vol. 91, № 434. P. 444–455. DOI: 10.2307/2291629.
59. Cattaneo M. D., Idrobo N., Titiunik R. A. Practical introduction to regression discontinuity designs: Foundations. Cambridge: Cambridge University Press, 2020. 120 p. ISBN 978-1-108-71020-6. DOI: 10.1017/9781108684606.
60. Кузьмина Ю. В. Метод разрывной регрессии и метод отбора подобного по вероятности для оценки эффекта одного года обучения: опыт применения на примере данных PISA 2009 // Социология: методология, методы, математическое моделирование. 2014, № 38. С. 7–37. EDN: SZSTOZ.
61. Abadie A. Semiparametric instrumental variable estimation of treatment response models // Journal of econometrics. 2003, vol. 113, № 2. P. 231–263. DOI: 10.1016/S0304-4076(02)00201-4.
62. Angrist J. D., Imbens G. W. Two-stage least squares estimation of average causal effects in models with variable treatment intensity // Journal of the American statistical association. 1995, vol. 90, № 430. P. 431–442. DOI: 10.1080/01621459.1995.10476535.
63. Angrist J. D., Krueger A. B. Empirical strategies in labor economics // Handbook of labor economics. 1999, vol. 3. P. 1277–1366. DOI: 10.1016/S1573-4463(99)03004-7.
64. Cattaneo M. D., Titiunik R. A. Regression discontinuity designs // Annual review of economics. 2022, vol. 14, № 1. P. 821–851. DOI: 10.1146/annurev-economics-051520-021409.
65. Cattaneo M. D., Idrobo N., Titiunik R. A. Practical introduction to regression discontinuity designs: Extensions. Cambridge: Cambridge University Press, 2024. 122 p. ISBN 978-1-009-46232-7. DOI: 10.1017/9781009441896.
66. Freedman B. Equipoise and the ethics of clinical research // The New England journal of medicine. 1987, vol. 317, № 3. P. 141–145. DOI: 10.1056/nejm198707163170304.
67. Liu J., Liu Z., Xu Y. A practical guide to estimating conditional marginal effects: Modern approaches. arXiv, 2025. 2504.01355v1. DOI: 10.48550/arXiv.2504.01355.
68. Wager S., Athey S. Estimation and inference of heterogeneous treatment effects using random forests // Journal of the American statistical association. 2018, vol. 113, № 523. P. 1228–1242. DOI: 10.1080/01621459.2017.1319839.
69. Brand J. E., Zhou X., Xie Y. Recent developments in causal inference and machine learning // Annual review of sociology. 2023, vol. 49. P. 81–110. DOI: 10.1146/annurev-soc-030420-015345.
70. Bansak K., Hainmueller J., Hopkins D. J., Yamamoto T. Conjoint survey experiments // Advances in experimental political science / Eds. by J. N. Druckman, D. P. Green. Cambridge: Cambridge University Press, 2021. P. 19–41. DOI: 10.1017/9781108777919.004.
71. VanderWeele T. J. Explanation in causal inference: Methods for mediation and interaction. New York: Oxford University Press, 2015. 728 p. ISBN 978-0-19-932587-0.
72. Bertrand M., Mullainathan S. Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination // American economic review. 2004, vol. 94, № 4. P. 991–1013. DOI: 10.1257/0002828042002561.
73. Bessudnov A., Shcherbak A. Ethnic discrimination in multi-ethnic societies: Evidence from Russia // European sociological review. 2020, vol. 36, № 1. P. 104–120. DOI: 10.1093/esr/jcz045.
74. Egami N., Imai K. Causal interaction in factorial experiments: Application to conjoint analysis // Journal of the American statistical association. 2019, vol. 114, № 526. P. 529–540. DOI: 10.1080/01621459.2018.1476246.
75. Imai K., Keele L., Tingley D. A general approach to causal mediation analysis // Psychological methods. 2010, vol. 15, № 4. P. 309–334. DOI: 10.1037/a0020761.
76. Tingley D., Yamamoto T., Hirose K., Keele L., Imai K. Mediation: R package for causal mediation analysis // Journal of statistical software. 2014, vol. 59, № 5. P. 1–38. DOI: 10.18637/jss.v059.i05.
77. Bellani L., Bia M. The long-run effect of childhood poverty and the mediating role of education // Journal of the Royal Statistical Society series A: Statistics in society. 2019, vol. 182, № 1. P. 37–68. DOI: 10.1111/rssa.12388.
78. Daniel R., Zhang J., Farewell D. Making apples from oranges: Comparing noncollapsible effect estimators and their standard errors after adjustment for different covariate sets // Biometrical journal. 2021, vol. 63, № 3. P. 528–557. DOI: 10.1002/bimj.201900297.
79. Morris T. P., Walker A. S., Williamson E. J., White I. R. Planning a method for covariate adjustment in individually randomised trials: A practical guide // Trials. 2022, vol. 23, № 1. 328. DOI: 10.1186/s13063-022-06097-z.
80. Greenland S. Noncollapsibility, confounding, and sparse-data bias. Part 1: The oddities of odds // Journal of clinical epidemiology. 2021, vol. 138. P. 178–181. DOI: 10.1016/j.jclinepi.2021.06.007.
81. Greifer N. Estimating effects after matching // Cran.R Project: [сайт]. 29.05.2025. URL: https://cran.r-project.org/web/packages/MatchIt/vignettes/estimating-effects.html (дата обращения: 05.10.2025).
82. Chernozhukov V., Hansen C. An IV model of quantile treatment effects // Econometrica. 2005, vol. 73, № 1. P. 245–261. DOI: 10.1111/j.1468-0262.2005.00570.x.
83. Abbring J. H., Heckman J. J. Econometric evaluation of social programs, part III: Distributional treatment effects, dynamic treatment effects, dynamic discrete choice, and general equilibrium policy evaluation // Handbook of econometrics. 2007, vol. 6. P. 5145–5303. DOI: 10.1016/S1573-4412(07)06072-2.
84. Lu J., Ding P., Dasgupta T. Treatment effects on ordinal outcomes: Causal estimands and sharp bounds // Journal of educational and behavioral statistics. 2018, vol. 43, № 5. P. 540–567. DOI: 10.3102/1076998618776435.
85. Di Francesco R., Mellace G. Causal inference for qualitative outcomes. arXiv, 2025. 2502.11691. DOI: 10.48550/arXiv.2502.11691.
86. Callaway B., Goodman-Bacon A., Sant'Anna P. H. Difference-in-differences with a continuous treatment. National Bureau of Economic Research, 2024. 59 p. w32117. DOI: 10.3386/w32117.
87. Imbens G. W. The role of the propensity score in estimating dose-response functions // Biometrika. 2000, vol. 87, № 3. P. 706–710. DOI: 10.1093/biomet/87.3.706.
88. Imai K., van Dyk D. A. Causal inference with general treatment regimes: Generalizing the propensity score // Journal of the American statistical association. 2004, vol. 99, № 467. P. 854–866. DOI: 10.1198/016214504000001187.
89. Вулдридж Д. М. Оценивание методом «разность разностей» // Квантиль. 2009, № 6. С. 15–23.
90. Baker A. C., Larcker D. F., Wang C. C. How much should we trust staggered difference-in-differences estimates? // Journal of financial economics. 2022, vol. 144, № 2. P. 370–395. DOI: 10.1016/j.jfineco.2022.01.004.
91. de Chaisemartin C., d'Haultfoeuille X. Two-way fixed effects and differences-in-differences with heterogeneous treatment effects: A survey // The econometrics journal. 2023, vol. 26, № 3. P. 1–30. DOI: 10.1093/ectj/utac017.
92. Roth J., Sant'Anna P. H., Bilinski A., Poe J. What's trending in difference-in-differences? A synthesis of the recent econometrics literature // Journal of econometrics. 2023, vol. 235, № 2. P. 2218–2244. DOI: 10.1016/j.jeconom.2023.03.008.
93. Xu Y. Causal inference with time-series cross-sectional data: A reflection // Oxford handbook of engaged methodological pluralism in political science (online edition) / Eds. by J. M. Box-Steffensmeier, D. P. Christenson, V. Sinclair-Chapman. 2023. 34 p. DOI: 10.1093/oxfordhb/9780192868282.013.30.
94. Liu L., Wang Y., Xu Y. A practical guide to counterfactual estimators for causal inference with time‐series cross‐sectional data // American journal of political science. 2024, vol. 68, № 1. P. 160–176. DOI: 10.1111/ajps.12723.
Статья
Поступила: 03.12.2023
Опубликована: 17.12.2025
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APA
Соколов, Б. О. (2025). Целевые каузальные эффекты в социальных исследованиях. Социология: методология, методы, математическое моделирование (Социология:4М), (61), 7-76. https://doi.org/10.19181/4m.2025.34.2.1
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СТАТИСТИЧЕСКИЕ МЕТОДЫ И АНАЛИЗ ДАННЫХ
