# Atlantic Causal Inference Conference (ACIC) Data Analysis Challenge 2017

arXiv: https://arxiv.org/abs/1905.09515 · HTML: https://mlsanity.com/paper/1905.09515/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 May 2019.

## Built on by (most cited first, 24 of 24)

- [Evaluation Methods and Measures for Causal Learning Algorithms](https://mlsanity.com/paper/2202.02896/) (2022)
- [TabPFN: One Model to Rule Them All?](https://mlsanity.com/paper/2505.20003/) (2025)
- [Generating Synthetic Text Data to Evaluate Causal Inference Methods](https://mlsanity.com/paper/2102.05638/) (2021)
- [Stabilized Inverse Probability Weighting via Isotonic Calibration](https://mlsanity.com/paper/2411.06342/) (2024)
- [Stochastic Tree Ensembles for Estimating Heterogeneous Effects](https://mlsanity.com/paper/2209.06998/) (2022)
- [A Mixing Time Lower Bound for a Simplified Version of BART](https://mlsanity.com/paper/2210.09352/) (2022)
- [Consistent Optimal Transport with Empirical Conditional Measures](https://mlsanity.com/paper/2305.15901/) (2023)
- [Doing Great at Estimating CATE? On the Neglected Assumptions in Benchmark Comparisons of Treatment Effect Estimators](https://mlsanity.com/paper/2107.13346/) (2021)
- [Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation](https://mlsanity.com/paper/2307.04988/) (2023)
- [Very fast Bayesian Additive Regression Trees on GPU](https://mlsanity.com/paper/2410.23244/) (2024)
- [DoTime: A Synthetic Benchmark Generator for Interventional and Counterfactual Time Series](https://mlsanity.com/paper/2607.27263/) (2026)
- [Estimating causal effects with optimization-based methods: A review and empirical comparison](https://mlsanity.com/paper/2203.00097/) (2022)
- [Hierarchical Bias-Driven Stratification for Interpretable Causal Effect Estimation](https://mlsanity.com/paper/2401.17737/) (2024)
- [IncomeSCM: From tabular data set to time-series simulator and causal estimation benchmark](https://mlsanity.com/paper/2405.16069/) (2024)
- [Benchmarking Estimators for Natural Experiments: A Novel Dataset and a Doubly Robust Algorithm](https://mlsanity.com/paper/2409.04500/) (2024)
- [A Bayesian Additive Regression Tree Model for Learning Conditional Average Treatment Effects in Regression Discontinuity Designs](https://mlsanity.com/paper/2503.00326/) (2025)
- [Tailoring Strictly Proper Scoring Rules for Downstream Tasks: An Application to Causal Inference](https://mlsanity.com/paper/2606.03332/) (2026)
- [Partially Specified Causal Simulations](https://mlsanity.com/paper/2309.10514/) (2023)
- [Identifiable Latent Bandits: Leveraging observational data for personalized decision-making](https://mlsanity.com/paper/2407.16239/) (2024)
- [Controllable Generative Sandbox for Causal Inference](https://mlsanity.com/paper/2603.03587/) (2026)
- [Representation Learning for Semiparametric Causal Mediation Analysis under No Essential Heterogeneity](https://mlsanity.com/paper/2607.10540/) (2026)
- [When Can You Trust Offline Evaluation of Equal-Cost Top-k Allocation? A Controlled, Reproducible Benchmark and Practitioner's Guide](https://mlsanity.com/paper/2608.12489/) (2026)
- [GeoACE: Geometry-Diverse Anchor-Correction Ensembles for Heterogeneous Treatment Effect Estimation](https://mlsanity.com/paper/2609.29974/) (2026)
- [What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views](https://mlsanity.com/paper/2609.36881/) (2026)

## Abstract (arXiv)

This brief note documents the data generating processes used in the 2017 Data Analysis Challenge associated with the Atlantic Causal Inference Conference (ACIC). The focus of the challenge was estimation and inference for conditional average treatment effects (CATEs) in the presence of targeted selection, which leads to strong confounding. The associated data files and further plots can be found on the first author's web page.