Paper introduces DRM for selecting robust CATE estimators.
problem Selecting CATE estimators without counterfactual outcomes.
method Distributionally Robust Metric (DRM) for CATE estimator selection.
result DRM selects robust CATE estimators robust to distribution shift.
New method improves CATE model selection with optimal regret rates.
problem Nontrivial task of selecting accurate CATE models.
method Causal Q-aggregation using doubly robust loss.
result Achieves optimal oracle model selection regret rates of log(M)/n.
Proposes a method to improve CATE estimation by imputing missing potential outcomes.
problem Statistical discrepancy between distinct treatment groups in CATE estimation.
method Contrastive learning approach to reliably impute missing potential outcomes for a subset of individuals.
result Improves the accuracy and robustness of CATE estimation models.
EBM reduces dimensionality for estimating heterogeneous CATEs.
problem Estimating CATEs requires many confounding variables, increasing sample complexity.
method Proposes an EBM that learns a low-dimensional representation of variables.
result EBM representations keep CATE estimates consistent and perform better than other methods.
Proposes K-Fold Causal BART for improved CATE estimation.
problem Improving estimation of Conditional Average Treatment Effects (CATE).
method K-Fold Causal Bayesian Additive Regression Trees (K-Fold Causal BART).
result K-Fold Causal BART is not state-of-the-art for ATE and CATE estimation in the IHDP dataset, but provides insights into model robustness and evaluation methods.
Method improves robustness and generalizability of CATE estimation.
problem Lack of external validity in site-specific models for diverse populations.
method Minimax-regret framework with robust optimization.
result Interpretable closed-form solution for generalizable CATE model.
Estimates CATEs using high-dimensional linear regression models.
problem Estimating individualized causal effects (CATEs) in two treatments.
method Proposes a Lasso regression method for consistently estimating CATEs under high-dimensional and non-sparse parameters, leveraging the assumption of implicit sparsity.
result The proposed method is consistent for estimating CATEs.
New method uses SHAP for biomarker identification in CATE models.
problem Identifying predictive biomarkers from observational data.
method Surrogate estimation approach using SHAP values for CATE meta-learners.
result SHAP accurately identifies biomarkers in high-dimensional data.
Modern CATE models often fail to outperform a trivial zero-effect predictor, highlighting significant challenges.
problem Lack of robustness in CATE models when applied to real-world data.
method Large-scale benchmark study using diverse observational sampling strategies and novel statistics.
result 62% of CATE estimates have higher MSE than a trivial zero-effect predictor, indicating poor performance.
New method combines randomization tests and flexible models for valid inference without splitting data.
problem Valid inference in randomized panel experiments with complex effect heterogeneity.
method Model-assisted randomization tests that estimate unsigned CATE from residualized outcomes.
result CATE-assisted tests control Type I error and achieve higher power than alternatives.
Estimates CATE under hidden confounding, accounting for bias and ignorance.
problem Learning CATE from high-dimensional data with unobserved confounders introduces bias and ignorance.
method Parametric interval estimator that accounts for hidden confounding and underrepresented samples.
result Estimator converges to tight bounds on CATE when there may be unobserved confounding.
Meta-learning improves CATE estimation from limited data.
problem Estimating heterogeneous treatment effects from scarce observational data.
method Meta-learning framework decomposes CATE estimation into sub-problems, using neural networks with shared and specific parameters, and optimizing task-specific parameters in closed form.
result Meta-learning method outperforms existing approaches in few-shot CATE estimation.
Proposes bounds on bias from low-dimensional representations in CATE estimation.
problem Bias in CATE estimation due to low-dimensional representations.
method Proposes a refutation framework to estimate bounds on representation-induced confounding bias.
result Demonstrates effectiveness of refutation framework in practice.
Proposes MRIV framework for unbiased CATE estimation using binary IVs.
problem Bias in estimating CATEs due to unobserved confounders.
method Multiply robust machine learning framework (MRIV) for binary IVs.
result MRIV yields multiple robust convergence rates and outperforms existing methods.
Method estimates CATE using RCT data to handle hidden confounders.
problem Estimating CATE in the presence of hidden confounders.
method Pseudo-confounder generator and CATE model alignment.
result Method reduces bias in CATE estimation.
Estimates CATEs for structured treatments using a new decomposition method.
problem Estimating conditional average treatment effects for complex data types.
method Generalized Robinson decomposition, isolating causal estimand, arbitrary model plugging, quasi-oracle convergence guarantee.
result Demonstrates superior performance in CATE estimation compared to prior work.
The paper estimates personalized treatment effects in medical settings with competing risks.
problem Estimating treatment effectiveness for specific events in the presence of alternative event types.
method Meta-learners combining Cox regression or random survival forests for risk modeling and elastic net regression or random forests for direct CATE modeling.
result Compared meta-learners in multiple simulation settings, providing practical guidance for model selection.
Study improves statistical inference for CATEs using Lasso and DML.
problem Estimating and inferring CATEs in high-dimensional settings.
method Doubly robust estimator, Lasso regularization, debiased Lasso, DML.
result TDL (triple/debiased Lasso) achieves n \sqrt{n} n -consistency and confidence intervals. Paper tackles CATE estimation with missing treatment info.
problem Challenges in estimating CATE with missing treatment information.
method Developed MTRNet, a novel CATE estimation algorithm using domain adaptation.
result Improves CATE estimation over state-of-the-art methods.
Framework improves CATE estimation by aligning active learning with causal objectives.
problem High cost of outcome measurements limits CATE estimation.
method Causal-EPIG framework, targeting unobservable causal quantities.
result Strategies outperform standard baselines, revealing context-dependent optimal approaches.
Method leverages data transfer for estimating CATE with KRR.
problem Leveraging findings from one study to estimate CATE in a different population.
method Overlap-adaptive transfer learning of CATE using kernel ridge regression.
result The method achieves superior efficiency and adaptability in estimating CATE.
New method improves CATE estimation in low overlap regions.
problem Low overlap in CATE estimation leads to poor performance of meta-learners.
method Overlap-Adaptive Regularization (OAR) that regularizes models proportionally to overlap weights.
result OAR significantly improves CATE estimation in low-overlap settings.
Optimal CATE estimation with structured contrast functions using KRR.
problem Estimating CATEs with complex response functions in RKHS.
method Unified two-stage kernel ridge regression method for structured contrast functions.
result Minimax rates governed by contrast function complexity, enabling adaptation.
We study the model selection problem in conditional average treatment effect (CATE) prediction. Unlike previous works on this topic, we focus on preserving the rank order of the performance of candidate CATE predictors to enable accurate and stable model selection. To this end, we analyze the model performance ranking …
End-to-end policy learning method improves CATE estimation.
problem Learning optimal treatment policies from partially observed data.
method Modified causal forest for policy learning.
result Maximizing policy value is equivalent to minimizing CATE.
A new method TNW-CATE estimates treatment effects using neural networks.
problem Estimating heterogeneous treatment effects with limited controls and many treatments.
method Trainable Nadaraya-Watson regression with shared parameters neural network.
result TNW-CATE outperforms traditional methods in various simulation experiments.
Paper introduces ps-BART for estimating nonlinear ATE and CATE in continuous treatments.
problem Estimating ATE and CATE in continuous treatments with nonlinear relationships.
method Generalized ps-BART model for nonparametric estimation.
result ps-BART outperforms BCF model in highly nonlinear settings.
This paper provides estimation and inference methods for a conditional average treatment effects (CATE) characterized by a high-dimensional parameter in both homogeneous cross-sectional and unit-heterogeneous dynamic panel data settings. In our leading example, we model CATE by interacting the base treatment variable w…
Paper introduces multi-scale methods to improve CATE estimation from EO data.
problem Challenges in balancing fine-grained and contextual information in EO-based causal inference.
method Multi-Scale Representation Concatenation, combining Vision Transformer and Causal Forests.
result Multi-scale approach captures effect heterogeneity better than single-scale models.
Two new ensemble methods improve CATE estimation across various scenarios.
problem Estimating CATE in clinical trials to understand treatment effects heterogeneity.
method Proposed two ensemble methods: Stacked X-Learner and Consensus Based Averaging (CBA).
result Ensemble methods achieve good performance across diverse scenarios.
QR-learner estimates individual treatment effects using external data.
problem Limited power to detect individual treatment effects in randomized trials.
method Model-agnostic learner that estimates conditional average treatment effects (CATE) using external data.
result QR-learner reduces mean squared error and can recover true CATE.
Proposes a framework to reconcile policy learning and profit maximization in CATE estimation.
problem Aligning CATE estimation with profit maximization for optimal customer treatment decisions.
method Optimizes a novel objective function that concentrates learning capacity near the decision boundary, ensuring consistency with the original profit function.
result Consistent CATE estimates can be recovered from existing profit-maximization pipelines, allowing firms to navigate the trade-off between accuracy and profit.
Combines IV and observational data to estimate CATEs with low compliance and unobserved confounding.
problem Estimating CATEs in personalized medicine and analytics with observational data and weak IVs.
method Two-stage framework: first learns biased CATEs from observational data, then corrects using IV data.
result Effective in estimating CATEs with low compliance and unobserved confounding.
New method combines CATE and CQTE to estimate treatment effects across different quantiles.
problem Challenges in estimating CQTE due to its dependence on smoothness of individual quantiles.
method Introduces a new estimand, the conditional quantile comparator (CQC), which retains information about the whole treatment distribution and leverages simplicity.
result Demonstrates improved accuracy in estimating treatment effects across different quantiles compared to existing methods.
We construct an infinitely exchangeable process on the set $\cate$ of subsets of the power set of the natural numbers N \mathbb{N} N via a Poisson point process with mean measure Λ Λ Λ on the power set of N \mathbb{N} N . Each $E\in\cate$ has a least monotone cover in $\catf$ , the collection of monotone subsets of $\cate$ , an…
Tree-based model averaging improves CATE estimation from diverse sites.
problem Limited sample size and privacy concerns prevent accurate personalized treatment effect estimation.
method Tree-based model averaging approach to estimate CATEs from multiple heterogeneous sites.
result Improved accuracy in estimating conditional average treatment effects (CATEs) across sites.
New method improves reliability of selecting individuals based on predicted treatment effects.
problem Reliability of selecting individuals based on predicted conditional average treatment effects (CATE) is unreliable.
method Denoised Conformal Alignment, combining proxy errors, variance estimation, and Benjamini-Hochberg selection.
result Significantly improved power in selecting individuals while maintaining false discovery rate control.
We study the problem of learning conditional average treatment effects (CATE) from observational data with unobserved confounders. The CATE function maps baseline covariates to individual causal effect predictions and is key for personalized assessments. Recent work has focused on how to learn CATE under unconfoundedne…
Paper tackles inconsistent CATE estimation across group assignments.
problem Inconsistent learning behavior for the same instance across different group assignments.
method CLAGA method to eliminate inconsistency.
result Significant performance improvements with CLAGA method.
Bayesian model estimates treatment effects near cutoffs in regression discontinuity designs.
problem Estimating conditional average treatment effects in regression discontinuity designs.
method Develops a Bayesian additive regression tree (BART) model with linear leaf-level regressions.
result Adapts to different slopes on the running variable near the cutoff, providing interpretable inference.
The paper introduces a privacy-preserving method for estimating treatment effects that maintains accuracy.
problem Estimating heterogeneous treatment effects in sensitive data while protecting privacy.
method A general meta-algorithm for CATE estimation with differential privacy guarantees, using sample splitting and parallel composition.
result The meta-algorithm maintains accuracy even with differential privacy, showing that most accuracy loss is due to variance increase.
GP-CATE calibrates CATE intervals in few-placebo trials with Gaussian processes.
problem Calibrating uncertainty intervals for CATE in small-arm trials.
method GP-CATE uses Gaussian processes to model each arm's outcome surface directly.
result GP-CATE achieves calibrated coverage where other methods fail.
Meta-learners estimate CATE from multiple environments with partial identification.
problem Estimating CATE from observational data across multiple environments with violations of causal assumptions.
method Adapt IV literature for partial identification, propose model-agnostic meta-learners.
result Meta-learners effectively estimate CATE bounds across various experiments.
The paper clarifies the distinction between CATE and ITE under ignorability assumptions.
problem Confusion between CATE and ITE hinders personalized effect estimation.
method Clarifies the distinction between CATE and ITE under ignorability assumptions.
result CATE and ITE are not necessarily the same under ignorability assumptions.
B-Learner provides bounds on CATE under hidden confounding risks.
problem Estimating CATE in the presence of hidden confounding.
method Adapting bounds on average treatment effect to conditional distributional treatment effects.
result B-Learner offers valid, sharp, efficient, and quasi-oracle bounds on CATE.
Framework tests CATE homogeneity across trials and evaluates confounding.
problem Assessing treatment effect consistency across randomized and observational studies.
method Leverages multiple randomized trials to test CATE homogeneity and compares with observational data.
result Identifies potential confounding and effect heterogeneity in treatment effects.
Study shows linear models can predict CATE without overfitting, even with large data.
problem Overfitting in large-scale causal inference models.
method Investigated linear models for CATE prediction, considering samples with switching distributions.
result IPW-learner converges risk to zero if propensity score is known, while T-learner fails to achieve consistency.
Algorithm identifies interpretable subgroups with elevated treatment effects.
problem Estimating high-dimensional, uninterpretable CATE results.
method Rule sets summarizing CATE estimates, optimizing subgroup size and effect size.
result Frontier of Pareto optimal rule sets for subgroup identification.