P-CRE-DML: A Novel Approach for Causal Inference in Non-Linear Panel Data

TL;DR

This paper presents P-CRE-DML, a new framework combining machine learning and panel data techniques to estimate causal effects in non-linear settings with unobserved heterogeneity, demonstrated through economic growth analysis.

Contribution

The paper introduces P-CRE-DML, a novel method that integrates proxy variables, correlated random effects, and double machine learning for improved causal inference in complex panel data.

Findings

Social trust positively impacts GDP growth.

P-CRE-DML outperforms CRE-DML and System GMM in bias reduction.

Method is applicable to various fields beyond economics.

Abstract

This paper introduces a novel Proxy-Enhanced Correlated Random Effects Double Machine Learning (P-CRE-DML) framework to estimate causal effects in panel data with non-linearities and unobserved heterogeneity. Combining Double Machine Learning (DML, Chernozhukov et al., 2018), Correlated Random Effects (CRE, Mundlak, 1978), and lagged variables (Arellano & Bond, 1991) and innovating within the CRE-DML framework (Chernozhukov et al., 2022; Clarke & Polselli, 2025; Fuhr & Papies, 2024), we apply P-CRE-DML to investigate the effect of social trust on GDP growth across 89 countries (2010-2020). We find positive and statistically significant relationship between social trust and economic growth. This aligns with prior findings on trust-growth relationship (e.g., Knack & Keefer, 1997). Furthermore, a Monte Carlo simulation demonstrates P-CRE-DML's advantage in terms of lower bias over CRE-DML and System GMM. P-CRE-DML offers a robust and flexible alternative for panel data causal inference, with applications beyond economic growth.