Estimation methods for identifying HCEs

In this section, we dive into sophisticated causal inference methodologies that are crucial for revealing the variable impacts of treatments across different subgroups within a population. Techniques such as regression discontinuity design (RDD), instrumental variables analysis, and propensity score matching (PSM) stand at the forefront. RDD capitalizes on a pre-set cutoff within an assignment variable to estimate causal effects near this threshold, simulating a randomized experiment environment. Instrumental variables analysis, on the other hand, addresses endogeneity and unobserved confounding by leveraging external instruments to uncover treatment effect heterogeneity. Meanwhile, PSM aims to reduce selection bias in observational studies, enabling a comparative analysis of treatment effects across varied strata or covariates.

These methods collectively enhance our understanding of how and why treatment effects differ among individuals or...