Papers › Estimating individual treatment effect: generalization bounds and algorithms
Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D. Johansson, David Sontag
There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine. We give a new theoretical analysis and family of algorithms for predicting individual treatment effect (ITE) from observational data, under the assumption known as strong ignorability. The algorithms learn a "balanced" representation such that the induced treated and control distributions look similar. We give a novel, simple and intuitive generalization-error bound showing that the expected ITE estimation error of a representation is bounded by a sum of the standard generalization-error of that representation and the distance between the treated and control distributions induced by the representation. We use Integral Probability Metrics to measure distances between distributions, deriving explicit bounds for the Wasserstein and Maximum Mean Discrepancy (MMD) distances. Experiments on real and simulated data show the new algorithms match or outperform the state-of-the-art.
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Tasks
Datasets
Introduced by this paper, per the archive.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Causal Inference | IHDP | Counterfactual Regression + WASS | Average Treatment Effect Error | 0.27 | #5 of 13 | Archive leaderboard | report |
| Causal Inference | IHDP | TARNet | Average Treatment Effect Error | 0.28 | #6 of 13 | Archive leaderboard | report |
| Causal Inference | IHDP | Causal Forest | Average Treatment Effect Error | 0.4 | #8 of 13 | Archive leaderboard | report |
| Causal Inference | IHDP | Balancing Neural Network | Average Treatment Effect Error | 0.42 | #9 of 13 | Archive leaderboard | report |
| Causal Inference | IHDP | k-NN | Average Treatment Effect Error | 0.79 | #11 of 13 | Archive leaderboard | report |
| Causal Inference | IHDP | Balancing Linear Regression | Average Treatment Effect Error | 0.93 | #12 of 13 | Archive leaderboard | report |
| Causal Inference | IHDP | Random Forest | Average Treatment Effect Error | 0.96 | #13 of 13 | Archive leaderboard | report |
| Causal Inference | Jobs | CFR MMD | Average Treatment Effect on the Treated Error | 0.08 | #3 of 5 | Archive leaderboard | report |
| Causal Inference | Jobs | CFR WASS | Average Treatment Effect on the Treated Error | 0.09 | #5 of 5 | Archive leaderboard | report |
| Heterogeneous Treatment Effect Estimation | IHDP | TARNet | PEHE | 0.95 | #2 of 2 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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