Research map / Probabilistic and scientific modeling / Treatment effects
Estimating individual treatment effect: generalization bounds and algorithms
Read the paperCheck your idea against 108,967 papers
AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Wasserstein IPM balance regularization (ablation) | TARNet on IHDP · IHDP within-sample | √PEHE: $.88 \pm .0$ -> $.71 \pm .0$ | Helped |
| Add Wasserstein IPM balance regularization (ablation) | TARNet on IHDP · IHDP out-of-sample | √PEHE: $.95 \pm .0$ -> $.76 \pm .0$ | Helped |
| Add MMD balance regularization (ablation) | TARNet on IHDP · IHDP within-sample | √PEHE: $.88 \pm .0$ -> $.73 \pm .0$ | Helped |
| Add MMD balance regularization (ablation) | TARNet on IHDP · IHDP out-of-sample | √PEHE: $.95 \pm .0$ -> $.78 \pm .0$ | Helped |
| Add Wasserstein IPM balance regularization (ablation) | TARNet on Jobs · Jobs within-sample | Policy risk: $.17 \pm .0$ -> $.17 \pm .0$ | No clear effect |
| Add Wasserstein IPM balance regularization (ablation) | TARNet on Jobs · Jobs out-of-sample | Policy risk: $.21 \pm .0$ -> $.21 \pm .0$ | No clear effect |
| Add MMD balance regularization (ablation) | TARNet on Jobs · Jobs within-sample | Policy risk: $.17 \pm .0$ -> $.18 \pm .0$ | Hurt |
| Add MMD balance regularization (ablation) | TARNet on Jobs · Jobs out-of-sample | Policy risk: $.21 \pm .0$ -> $.21 \pm .0$ | No clear effect |
Closest papers by meaning
- Impossibility of Distribution-Free Predictive Inference for Individual Treatment Effects (NeurIPS 2026 · similarity 0.90)
- Learning Representations of Instruments for Partial Identification of Treatment Effects (ICML 2025 · similarity 0.89)
- An effective framework for estimating individualized treatment rules (NeurIPS 2024 · similarity 0.88)
- PairNet: Training with Observed Pairs to Estimate Individual Treatment Effect (ICML 2024 · similarity 0.87)
- Conformal Inference of Individual Treatment Effects Using Conditional Density Estimates (AAAI 2025 · similarity 0.87)
- Towards Estimating Bounds on the Effect of Policies under Unobserved Confounding (NeurIPS 2024 · similarity 0.87)
Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.