# Ensemble Methods for Causal Effects in Panel Data Settings

## TL;DR

This paper introduces an ensemble approach for estimating causal effects in panel data, demonstrating improved performance over individual methods like regression, synthetic control, and matrix completion across economic datasets.

## Contribution

It proposes an ensemble method that combines existing approaches, showing enhanced accuracy in causal effect estimation in panel data settings.

## Key findings

- Ensemble methods outperform individual methods in several economic datasets.
- Matrix completion often receives the highest weight in the ensemble.
- Ensemble methods are a promising direction for future causal inference research.

## Abstract

This paper studies a panel data setting where the goal is to estimate causal effects of an intervention by predicting the counterfactual values of outcomes for treated units, had they not received the treatment. Several approaches have been proposed for this problem, including regression methods, synthetic control methods and matrix completion methods. This paper considers an ensemble approach, and shows that it performs better than any of the individual methods in several economic datasets. Matrix completion methods are often given the most weight by the ensemble, but this clearly depends on the setting. We argue that ensemble methods present a fruitful direction for further research in the causal panel data setting.