A Way to Synthetic Triple Difference
This paper discusses a practical approach that combines synthetic control with triple difference to address violations of the parallel trends assumption. By transforming triple difference into a DID structure, we can apply synthetic control to a triple-difference framework, enabling more robust estimates when parallel trends are violated across multiple dimensions. The proposed procedure is applied to a real-world dataset to illustrate when and how we should apply this practice, while cautions are presented afterwards. This method contributes to improving causal inference in policy evaluations and offers a valuable tool for researchers dealing with heterogeneous treatment effects across subgroups.
Code (0)
등록된 구현이 없습니다.
Tasks
Causal InferenceMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Triple Instrumented Difference-in-Differences
In this paper, we formalize a triple instrumented difference-in-differences (DID-IV). In this design, a triple Wald-DID estimand, which divides the difference-in-difference-in-differences (DDD) estimand of the outcome by…
Comparing Contrastive and Triplet Loss: Variance Analysis and Optimization Behavior
Contrastive loss and triplet loss are widely used objectives in deep metric learning, yet their effects on representation quality remain insufficiently understood. We present a theoretical and empirical comparison of the…
Metric LearningTriple Difference Designs with Heterogeneous Treatment Effects
Triple difference designs have become increasingly popular in empirical economics. The advantage of a triple difference design is that, within a treatment group, it allows for another subgroup of the population -- potent…
Semiparametric Triple Difference Estimators
The triple difference causal inference framework is an extension of the well-known difference-in-differences framework. It relaxes the parallel trends assumption of the difference-in-differences framework through leverag…
Causal InferenceCausal Duration Analysis with Diff-in-Diff
In economic program evaluation, it is common to obtain panel data in which outcomes are indicators that an individual has reached an absorbing state. For example, they may indicate whether an individual has exited a peri…