Regression Discontinuity Design with Multiple Groups for Heterogeneous Causal Effect Estimation

TL;DR

This paper introduces a novel method for estimating heterogeneous causal effects using multiple regression discontinuity datasets with different thresholds, allowing for effect estimation at any point between thresholds while adjusting for covariate differences.

Contribution

The paper develops an augmented inverse probability weighted local linear estimator for RD designs with multiple groups, enabling flexible effect estimation across thresholds.

Findings

Estimator performs well in finite sample simulations.

Method effectively adjusts for covariate distribution differences.

Allows estimation of effects at arbitrary points between thresholds.

Abstract

We propose a new estimation method for heterogeneous causal effects which utilizes a regression discontinuity (RD) design for multiple datasets with different thresholds. The standard RD design is frequently used in applied researches, but the result is very limited in that the average treatment effects is estimable only at the threshold on the running variable. In application studies it is often the case that thresholds are different among databases from different regions or firms. For example thresholds for scholarship differ with states. The proposed estimator based on the augmented inverse probability weighted local linear estimator can estimate the average effects at an arbitrary point on the running variable between the thresholds under mild conditions, while the method adjust for the difference of the distributions of covariates among datasets. We perform simulations to investigate the performance of the proposed estimator in the finite samples.