Целевые каузальные эффекты в социальных исследованиях

Социология: методология, методы, математическое моделирование. 2025. № 61. С. 7–76.

Sokolov B.

This article reviews a set of estimands commonly used in modern applied research to operationalize causal inquiries within the Rubin Causal Model (RCM). I first introduce the basic average treatment effects (ATE, ATT, ATC) and then describe their main extensions, including local and conditional treatment effects, causal interactions, causal mediation, multivalued or continuous treatments, and longitudinal treatment effects. For each estimand, I provide a substantive explanation along with examples of research questions it can address. For the most popular estimands, I also discuss the key assumptions required for their nonparametric identification and statistical methods available for their estimation.

Keywords: quantitative methods, эффект воздействия, treatment effect, causal inference, доказательная политика, количественные методы, evidence-based policy, Rubin Causal Model, estimand, статистический причинно-следственный вывод, каузальная модель Рубина, эстиманд

Publication based on the results of: Comparative studies of culture and subjective well-being: differentiation and integration of societies in the context of global challenges (2025)