# Estimating Treatment Effects with Causal Forests: An Application

arXiv: https://arxiv.org/abs/1902.07409 · HTML: https://mlsanity.com/paper/1902.07409/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2019.

## Built on by (most cited first, 30 of 58)

- [Causal Effect Inference for Structured Treatments](https://mlsanity.com/paper/2106.01939/) (2021)
- [Empirical Analysis of Model Selection for Heterogeneous Causal Effect Estimation](https://mlsanity.com/paper/2211.01939/) (2022)
- [DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions](https://mlsanity.com/paper/2108.13518/) (2021)
- [Causal isotonic calibration for heterogeneous treatment effects](https://mlsanity.com/paper/2302.14011/) (2023)
- [End-to-End Cost-Effective Incentive Recommendation under Budget Constraint with Uplift Modeling](https://mlsanity.com/paper/2408.11623/) (2024)
- [BITES: Balanced Individual Treatment Effect for Survival data](https://mlsanity.com/paper/2201.03448/) (2022)
- [Using Non-Linear Causal Models to Study Aerosol-Cloud Interactions in the Southeast Pacific](https://mlsanity.com/paper/2110.15084/) (2021)
- [Variable importance for causal forests: breaking down the heterogeneity of treatment effects](https://mlsanity.com/paper/2308.03369/) (2023)
- [Optimal Treatment Regimes for Proximal Causal Learning](https://mlsanity.com/paper/2212.09494/) (2022)
- [Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators](https://mlsanity.com/paper/2107.13346/) (2021)
- [Heterogeneous Treatment Effect Estimation using machine learning for Healthcare application: tutorial and benchmark](https://mlsanity.com/paper/2109.12769/) (2021)
- [Learning Optimal Dynamic Treatment Regimes Using Causal Tree Methods in Medicine](https://mlsanity.com/paper/2204.07124/) (2022)
- [What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?](https://mlsanity.com/paper/2206.10323/) (2022)
- [Comparison of Methods that Combine Multiple Randomized Trials to Estimate Heterogeneous Treatment Effects](https://mlsanity.com/paper/2303.16299/) (2023)
- [Inferring Heterogeneous Treatment Effects of Crashes on Highway Traffic: A Doubly Robust Causal Machine Learning Approach](https://mlsanity.com/paper/2401.00781/) (2024)
- [Optimizing Multi-Scale Representations to Detect Effect Heterogeneity Using Earth Observation and Computer Vision: Applications to Two Anti-Poverty RCTs](https://mlsanity.com/paper/2411.02134/) (2024)
- [Measuring Variable Importance in Heterogeneous Treatment Effects with Confidence](https://mlsanity.com/paper/2408.13002/) (2024)
- [Robust Uplift Modeling with Large-Scale Contexts for Real-time Marketing](https://mlsanity.com/paper/2502.15697/) (2025)
- [Causal Knowledge Transfer for Multi-Agent Reinforcement Learning in Dynamic Environments](https://mlsanity.com/paper/2507.13846/) (2025)
- [How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign](https://mlsanity.com/paper/2204.10820/) (2022)
- [A Unified Framework of Policy Learning for Contextual Bandit with Confounding Bias and Missing Observations](https://mlsanity.com/paper/2303.11187/) (2023)
- [Fairness Implications of Heterogeneous Treatment Effect Estimation with Machine Learning Methods in Policy-making](https://mlsanity.com/paper/2309.00805/) (2023)
- [Transparency challenges in policy evaluation with causal machine learning -- improving usability and accountability](https://mlsanity.com/paper/2310.13240/) (2023)
- [DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation](https://mlsanity.com/paper/2406.00611/) (2024)
- [CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect](https://mlsanity.com/paper/2407.01004/) (2024)
- [DeCaf: A Causal Decoupling Framework for OOD Generalization on Node Classification](https://mlsanity.com/paper/2410.20295/) (2024)
- [Model Agnostic Differentially Private Causal Inference](https://mlsanity.com/paper/2505.19589/) (2025)
- [Deep PQR: Solving Inverse Reinforcement Learning using Anchor Actions](https://mlsanity.com/paper/2007.07443/) (2020)
- [Moderately-Balanced Representation Learning for Treatment Effects with Orthogonality Information](https://mlsanity.com/paper/2209.01956/) (2022)
- [Targeting relative risk heterogeneity with causal forests](https://mlsanity.com/paper/2309.15793/) (2023)

## Abstract (arXiv)

We apply causal forests to a dataset derived from the National Study of Learning Mindsets, and consider resulting practical and conceptual challenges. In particular, we discuss how causal forests use estimated propensity scores to be more robust to confounding, and how they handle data with clustered errors.