# Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge

arXiv: https://arxiv.org/abs/1907.07592 · HTML: https://mlsanity.com/paper/1907.07592/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 12 of 12)

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- [CausalKANs: interpretable treatment effect estimation with Kolmogorov-Arnold networks](https://mlsanity.com/paper/2509.22467/) (2025)
- [A Double Machine Learning Trend Model for Citizen Science Data](https://mlsanity.com/paper/2210.15524/) (2022)
- [Extended Fiducial Inference for Individual Treatment Effects via Deep Neural Networks](https://mlsanity.com/paper/2505.01995/) (2025)
- [Policy learning for many outcomes of interest: Combining optimal policy trees with multi-objective Bayesian optimisation](https://mlsanity.com/paper/2212.06312/) (2022)
- [Debiased Machine Learning for Conformal Prediction of Counterfactual Outcomes Under Runtime Confounding](https://mlsanity.com/paper/2604.03772/) (2026)

## Abstract (arXiv)

A growing number of methods aim to assess the challenging question of treatment effect variation in observational studies. This special section of "Observational Studies" reports the results of a workshop conducted at the 2018 Atlantic Causal Inference Conference designed to understand the similarities and differences across these methods. We invited eight groups of researchers to analyze a synthetic observational data set that was generated using a recent large-scale randomized trial in education. Overall, participants employed a diverse set of methods, ranging from matching and flexible outcome modeling to semiparametric estimation and ensemble approaches. While there was broad consensus on the topline estimate, there were also large differences in estimated treatment effect moderation. This highlights the fact that estimating varying treatment effects in observational studies is often more challenging than estimating the average treatment effect alone. We suggest several directions for future work arising from this workshop.