Methodological Foundations of Modern Causal Inference in Social Science Research

TL;DR

This paper reviews modern causal inference methodologies in social science, focusing on assumptions, strategies for violations, and asymptotic analysis of estimators used with observational data.

Contribution

It provides a comprehensive overview of causal inference methods, assumptions, and asymptotic properties relevant to social science research using observational data.

Findings

Reviewed assumptions for causal identification

Discussed strategies for assumption violations

Analyzed asymptotic properties of treatment effect estimators

Abstract

This paper serves as a literature review of methodology concerning the (modern) causal inference methods to address the causal estimand with observational/survey data that have been or will be used in social science research. Mainly, this paper is divided into two parts: inference from statistical estimand for the causal estimand, in which we reviewed the assumptions for causal identification and the methodological strategies addressing the problems if some of the assumptions are violated. We also discuss the asymptotical analysis concerning the measure from the observational data to the theoretical measure and replicate the deduction of the efficient/doubly robust average treatment effect estimator, which is commonly used in current social science analysis.