The paper resolves the paradox of using unlabeled data for treatment effect estimation.
problem Using unlabeled data to estimate propensity scores for treatment effect estimation.
method Proposes a simple procedure to reconcile the use of estimated propensity scores with the advice to use true propensity scores.
result Direct regression may be preferable to inverse-propensity weighting in many circumstances.
This study optimizes covariate density and propensity score for efficient ATE estimation.
problem Efficiently estimating average treatment effects (ATEs) with minimal variance.
method Adaptive experiment optimizing both covariate density and propensity score.
result Proposed method minimizes the semiparametric efficiency bound for ATE estimation.
The paper investigates how calibrating propensity scores improves DML estimates of average treatment effects.
problem Improving the accuracy of DML estimates in finite samples.
method Propensity score calibration within the Double/debiased machine learning framework.
result Calibrating propensity scores reduces the root mean squared error of DML estimates of average treatment effects in finite samples.
Proposes a novel neural network method to estimate average treatment effect.
problem Bias in estimating average treatment effect due to confounding and instrumental variables.
method Self-balancing neural network (Sbnet) that estimates pseudo propensity scores and average treatment effect in one step.
result Proposed method outperforms state-of-the-art methods in simulations and real-world datasets.
Proposes a method to stabilize treatment effect estimation with unbalanced data.
problem Unbalanced treatment assignment leading to unstable propensity score estimations.
method Undersamples data for propensity score modeling and calibrates scores to match original distribution.
result The estimator retains asymptotic properties of the DML estimator and improves finite sample performance.
Ablation studies show BCF model's propensity score is not essential for treatment effect estimation.
problem Understanding the necessity of propensity score in nonparametric treatment effect estimation.
method Partial ablation studies of Bayesian Causal Forest (BCF) model.
result Excluding estimated propensity score does not affect treatment effect estimation or uncertainty quantification.
Proposes stabilized weights for causal inference using isotonic calibration.
problem Stability and bias issues in inverse propensity weighting.
method Post-hoc isotonic calibration of inverse propensity weights.
result Improves performance of doubly robust estimators of average treatment effect.
Study designs for estimating treatment effects in adaptive experiments.
problem Estimating treatment effects under adaptive treatment assignment.
method Propose and analyze IPW and AIPW estimators, establish CLTs under design stability.
result Central limit theorems for IPW and AIPW estimators under design stability.
CARD detects treatment responders with machine learning and adjustment.
problem Identifying responders in non-random treatment settings.
method Conformal prediction, machine learning, propensity score adjustment.
result High power responder detection in various scenarios.
New neural network model improves treatment effect estimation.
problem Estimating treatment effects from observational data.
method Proposes a neural network model leveraging covariates and neighboring instances.
result Reports better treatment effect estimation performance.
Q-Learner estimates ratio-based treatment effects without imposing parametric structures.
problem Estimating treatment effects as ratios in non-linear settings.
method Decomposes ratio-CATE into two classification tasks, using doubly robust augmentations.
result Q-Learner outperforms other methods in low-conversion and observational data settings.
In this paper, we propose deep learning techniques for econometrics, specifically for causal inference and for estimating individual as well as average treatment effects. The contribution of this paper is twofold: 1. For generalized neighbor matching to estimate individual and average treatment effects, we analyze the …
Post-calibration improves the accuracy of causal effect estimation.
problem Improperly calibrated propensity scores lead to inaccurate causal effect estimation.
method Performed a simulation study to assess the impact of post-calibration on causal effect estimation.
result Post-calibration reduces the error in estimating the average treatment effect, especially for expressive uncalibrated statistical estimators.
FIDDLE uses deep learning to estimate ATE from complex data.
problem Estimating ATE from high-dimensional, correlated covariates with sparse nonlinear effects.
method Factor-augmented deep learning for propensity and outcome models.
result FIDDLE consistently estimates ATE under model misspecification and is semiparametrically efficient.
Proposes a new model for estimating individual treatment effects.
problem Estimating individual treatment effects from observational data is challenging.
method Integrates diffusion modeling and conformal inference with propensity score and covariate approximation.
result Establishes rigorous theoretical guarantees and demonstrates competitive performance.
Proposes new method for calibrating treatment effect predictors.
problem Calibrating predictors of heterogeneous treatment effects.
method Causal isotonic calibration and cross-calibration.
result Achieves fast calibration rates under weak conditions.
Study introduces a new framework for policy learning without positivity assumption.
problem Learning optimal treatment assignment policies from observational data with constraints.
method Incremental propensity score policies and semiparametric efficiency theory.
result Validated framework's performance through numerical experiments.
In this paper, we propose a robust method to estimate the average treatment effects in observational studies when the number of potential confounders is possibly much greater than the sample size. We first use a class of penalized M-estimators for the propensity score and outcome models. We then calibrate the initial e…
The paper learns personalized treatment rules from observational data.
problem Developing effective treatment policies for individual patients.
method Contextual bandit approach to minimize expected risk of treatment policies.
result The proposed method outperforms physicians and baseline approaches in IV and VP administration.
Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label. The result produced from the process is a set of instance-specific feature perturbations, or recommendations, that optimally improve the probability of the class label. In this work…
New method improves causal effect estimation by addressing imbalance in training data.
problem Imbalance between treatment and control groups in training data.
method Combines distributionally robust optimization and weight regularization.
result Consistent improvements over existing methods in experiments.
New method for estimating treatment effects without complex propensity models.
problem Estimating treatment effects in dynamic treatment regimes.
method Recursive Riesz representer estimation for de-biasing corrections.
result Directly estimates de-biasing corrections without auxiliary models.
Paper proposes a new method for estimating treatment effects using interpretable deep learning models.
problem Estimating treatment effects from observational data with interpretability.
method Proposes a novel objective function using energy distance balancing score and neural additive models for improved interpretability.
result Demonstrates superior performance over state-of-the-art methods in semi-synthetic experiments.
Two new methods generate probabilistic forecasts of individual treatment effects.
problem Generating probabilistic forecasts of individual treatment effects for risk-aware decision-making.
method Proposes CCT and CMC meta-learners combining conformal predictive systems with analytic convolution or Monte Carlo sampling.
result Achieve probabilistically calibrated predictive distributions and performant continuous ranked probability scores.
Many estimators of the average effect of a treatment on an outcome require estimation of the propensity score, the outcome regression, or both. It is often beneficial to utilize flexible techniques such as semiparametric regression or machine learning to estimate these quantities. However, optimal estimation of these r…
This paper addresses the use of neural networks for the estimation of treatment effects from observational data. Generally, estimation proceeds in two stages. First, we fit models for the expected outcome and the probability of treatment (propensity score) for each unit. Second, we plug these fitted models into a downs…
This tutorial introduces causal modeling methods for researchers.
problem Understanding causal relationships in research studies.
method Integrates potential outcomes and graphical methods for causal modeling.
result Clear notation and practical examples for applied researchers.
Enhances credit card limit adjustments by considering treatment uncertainty and prediction criteria.
problem Optimal treatment selection under multitreatment scenarios.
method Proposes a comprehensive methodology incorporating conditional value-at-risk and prediction criterion for continuous outcomes.
result Significantly improved policy performance in credit card limit adjustments.
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first estima…
Study improves machine learning for estimating survival treatment effects.
problem Estimating heterogeneous survival treatment effects in observational data.
method Flexible machine learning methods in the counterfactual framework, including AFT-BART-NP.
result AFT-BART-NP consistently yields best performance in terms of bias, precision, and frequentist coverage.
New method estimates stochastic intervention effects in decision-making domains.
problem Current causal inference methods are limited to deterministic treatment, unable to handle stochastic policies.
method Developed a new stochastic propensity score and stochastic intervention effect estimator (SIE) with a customized genetic algorithm (Ge-SIO).
result Empirical study shows significant performance improvement over state-of-the-art baselines.
DOPE efficiently estimates ATE with complex covariates.
problem Efficient estimation of ATE from complex covariates.
method Proposed DOPE framework for efficient adjustment.
result DOPE retains efficiency even with highly predictive covariates.
New method offsets DML's error-compounding issue and provides more stable causal parameter estimates.
problem Estimating ATE from observational data with robustness and stability.
method Robust Causal Learning (RCL) method to offset DML's deficiencies.
result RCL estimators are more stable and perform better than DML and traditional estimators.
The study provides a theory for causal machine learning with generalization bounds.
problem Lack of theoretical guarantees for causal machine learning algorithms.
method Introduces a novel change-of-measure inequality to bound model loss.
result Tight bounds on model loss in terms of treatment propensities deviation.
Proposes rounding method for precise treatment effect estimation under budget constraints.
problem Resource-constrained experimental design for precise treatment effect estimation.
method Dependent randomized rounding procedure to convert assignment probabilities into binary treatment decisions.
result Improved estimator precision through variance reduction and efficient inference.
In biostatistics, propensity score is a common approach to analyze the imbalance of covariate and process confounding covariates to eliminate differences between groups. While there are an abundant amount of methods to compute propensity score, a common issue of them is the corrupted labels in the dataset. For example,…
Proposes a robust estimator for high-dimensional data with heterogeneous treatment effects.
problem Estimating heterogeneous treatment effects with many more regressors than observations.
method Doubly robust two-stage semiparametric difference-in-difference estimator using machine learning for propensity score estimation.
result Valid inference for heterogeneous treatment effects with bias correction procedures.
New method reduces confidence interval sizes for causal inference.
problem Inaccurate propensity scores and extreme scores cause large confidence intervals.
method Data-dependent Coarse IPW (CIPW) estimators.
result Robust CIPW estimators reduce confidence interval sizes to ε+1/√n.
Paper presents a method to align unpaired samples across different modalities.
problem Challenges in collecting paired samples for multimodal representation learning.
method Uses propensity score alignment based on Rubin's framework to estimate a common space for unpaired samples.
result Optimal transport matching significantly improves alignment in real-world data.
Propensity score matching improves fairness in machine learning models.
problem Bias in training data affects fairness metrics in machine learning models.
method Propensity score matching to evaluate and mitigate bias in test data.
result FairMatch significantly reduces bias in test data without sacrificing predictive performance.
The paper develops a method for self-normalized inference in adaptive experiments.
problem Adaptive experiments require a fixed horizon for ATE estimation, but propensities can change.
method The method uses self-normalized martingale limit theory to estimate ATE.
result The Studentized statistic is asymptotically N(0,1) at the prespecified horizon.
Proposes RCL method to improve ATE estimation from observational data.
problem Error-compounding issue and extreme estimates in DML estimators.
method Robust Causal Learning (RCL) method to offset DML deficiencies.
result RCL estimators are more stable and perform better than DML estimators.
New algorithm improves causal effect estimation for continuous treatments.
problem Observational causal inference with continuous treatments.
method End-to-end entropy balancing for maximizing causal inference accuracy.
result Our algorithm estimates causal effect more accurately than baseline.
GEAR uses auxiliary data to estimate optimal decisions in studies with limited primary outcomes.
problem Estimating optimal decisions when primary outcomes are not available in experimental samples.
method GEAR uses augmented inverse propensity weighting to estimate optimal decisions based on auxiliary data.
result GEAR estimators and value estimators have established asymptotic properties and are validated in simulations and a real application.
Adaptive experiment designs can dramatically improve statistical efficiency in randomized trials, but they also complicate statistical inference. For example, it is now well known that the sample mean is biased in adaptive trials. Inferential challenges are exacerbated when our parameter of interest differs from the pa…
A2A metric evaluates bias correction methods, reducing ATE estimation errors.
problem Selection biases in non-randomized studies of medical treatments.
method Propensity score matching (PSM) with novel metric A2A.
result Reduces ATE estimation errors by up to 90% across synthetic and real-world datasets.
New confidence intervals improve treatment effect estimation in randomized experiments.
problem Improving confidence intervals for treatment effects in randomized experiments.
method Systematic exploitation of negative dependence or variance adaptivity.
result Achieved nonasymptotic confidence intervals with the same effective sample size as asymptotic ones.
Neural score matching improves high-dimensional causal inference by using neural networks for balancing scores.
problem Impracticality of traditional matching methods in high-dimensional datasets due to the curse of dimensionality.
method Develops neural networks to create non-trivial, multivariate balancing scores for high-dimensional causal inference.
result Neural score matching outperforms other methods in treatment effect estimation and reducing imbalance on high-dimensional datasets.