# What Is the Value Added by Using Causal Machine Learning Methods in a Welfare Experiment Evaluation?

## TL;DR

This paper evaluates whether causal machine learning methods improve the estimation of treatment effects in a welfare experiment, finding they support theoretical predictions but also have limitations compared to conventional estimators.

## Contribution

It provides a re-evaluation of CML methods in a welfare experiment context, highlighting their support for theory and discussing their limitations.

## Key findings

- CML methods support theoretical labor supply predictions
- Some conventional CATE estimators fail in this context
- CML methods have specific limitations discussed in the paper

## Abstract

Recent studies have proposed causal machine learning (CML) methods to estimate conditional average treatment effects (CATEs). In this study, I investigate whether CML methods add value compared to conventional CATE estimators by re-evaluating Connecticut's Jobs First welfare experiment. This experiment entails a mix of positive and negative work incentives. Previous studies show that it is hard to tackle the effect heterogeneity of Jobs First by means of CATEs. I report evidence that CML methods can provide support for the theoretical labor supply predictions. Furthermore, I document reasons why some conventional CATE estimators fail and discuss the limitations of CML methods.