# Causal Machine Learning for Moderation Effects

arXiv: https://arxiv.org/abs/2401.08290 · HTML: https://mlsanity.com/paper/2401.08290/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 63 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 13 of 13)

- [Estimation and Inference of Heterogeneous Treatment Effects using Random Forests](https://mlsanity.com/paper/1510.04342/) (2015)
- [Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning](https://mlsanity.com/paper/1706.03461/) (2017)
- [Quasi-Oracle Estimation of Heterogeneous Treatment Effects](https://mlsanity.com/paper/1712.04912/) (2017)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Nonparametric methods for doubly robust estimation of continuous treatment effects](https://mlsanity.com/paper/1507.00747/) (2015)
- [RieszNet and ForestRiesz: Automatic Debiased Machine Learning with Neural Nets and Random Forests](https://mlsanity.com/paper/2110.03031/) (2021)
- [De-Biased Machine Learning of Global and Local Parameters Using Regularized Riesz Representers](https://mlsanity.com/paper/1802.08667/) (2018)
- [Automatic Debiased Machine Learning for Covariate Shifts](https://mlsanity.com/paper/2307.04527/) (2023)
- [Estimation and Inference with Trees and Forests in High Dimensions](https://mlsanity.com/paper/2007.03210/) (2020)
- [High-Dimensional L2Boosting: Rate of Convergence](https://mlsanity.com/paper/1602.08927/) (2016)
- [Priority to unemployed immigrants? A causal machine learning evaluation of training in Belgium](https://mlsanity.com/paper/1912.12864/) (2019)
- [Double Machine Learning for Static Panel Models with Fixed Effects](https://mlsanity.com/paper/2312.08174/) (2023)
- [Nonparametric Causal Decomposition of Group Disparities](https://mlsanity.com/paper/2306.16591/) (2023)

## Abstract (arXiv)

It is valuable for any decision maker to know the impact of decisions (treatments) on average and for subgroups. The causal machine learning literature has recently provided tools for estimating group average treatment effects (GATE) to better describe treatment heterogeneity. This paper addresses the challenge of interpreting such differences in treatment effects between groups while accounting for variations in other covariates. We propose a new parameter, the balanced group average treatment effect (BGATE), which measures a GATE with a specific distribution of a priori-determined covariates. By taking the difference between two BGATEs, we can analyze heterogeneity more meaningfully than by comparing two GATEs, as we can separate the difference due to the different distributions of other variables and the difference due to the variable of interest. The main estimation strategy for this parameter is based on double/debiased machine learning for discrete treatments in an unconfoundedness setting, and the estimator is shown to be √N-consistent and asymptotically normal under standard conditions. We propose two additional estimation strategies: automatic debiased machine learning and a specific reweighting procedure. Last, we demonstrate the usefulness of these parameters in a small-scale simulation study and in an empirical example.