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.
We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for the bias is the inverse propensity score (IPS) estimation. However, the performance of existing propensity-based methods can suffer significantly from the propensity estimation bias. I…
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.
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.
Improves robustness of propensity score estimators in challenging settings.
problem Limited overlap, small sample sizes, or unbalanced data.
method Extends calibration techniques for propensity score models, focusing on sample-splitting schemes.
result Calibration reduces variance and bias in inverse probability weighting and double/debiased machine learning frameworks.
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.
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.
Observational cohort studies with oversampled exposed subjects are typically implemented to understand the causal effect of a rare exposure. Because the distribution of exposed subjects in the sample differs from the source population, estimation of a propensity score function (i.e., probability of exposure given basel…
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.
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.
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.
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…
Presentation bias is one of the key challenges when learning from implicit feedback in search engines, as it confounds the relevance signal with uninformative signals due to position in the ranking, saliency, and other presentation factors. While it was recently shown how counterfactual learning-to-rank (LTR) approache…
Proposes methods to estimate posterior probability and propensity score functions without assuming constant propensity score.
problem Learning from biased positive-unlabeled data.
method Parametric approach to joint estimation of posterior probability and propensity score functions using maximum likelihood and alternating maximization.
result Proposed methods are comparable or better than existing methods based on Expectation-Maximisation scheme.
In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus missing-not-at-random. Developing a method to facilitate the learning of a recommender with biased feedback is one of the most challenging problems, as it is widely known that naive approaches under sel…
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 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.
Unified causal inference framework using distribution adaptation.
problem Estimating Average Treatment Effects (ATE) under uncertainty in propensity scores.
method Reframed as domain adaptation problem, using machine learning techniques.
result Joint Robust Estimator (JRE) achieves up to 15% reduction in MSE.
Improved A/B testing by leveraging system similarities.
problem Traditional A/B testing ignores potential system similarities.
method Off-policy estimation to exploit system propensities.
result Improved A/B testing estimators achieve better accuracy.
The study explores machine learning for predicting customer propensity-to-pay uncertainty.
problem Improving customer experience, reducing financial hardship, and managing cash flow risks.
method Investigated machine learning models for predicting propensity-to-pay, focusing on uncertainty estimation.
result Novel Bayesian Neural Network model for binary classification of propensity-to-pay.
The paper develops methods to handle missing data using regularized M-estimation in reproducing kernel Hilbert space.
problem Handling missing data in statistical analysis.
method Kernel ridge regression for imputation and maximum entropy method for propensity score estimation.
result The proposed methods achieve statistical consistency and asymptotic equivalence.
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.
A new tensor completion method handles missing data with missing not at random entries.
problem Handling missing data in tensors where the probability of observation depends on other entries.
method Estimate propensities using convex relaxation, then use higher-order SVD with inverse propensities weights.
result Finite-sample error bounds on the completed tensor are provided.
Novel LSE estimator improves off-policy learning and evaluation.
problem High variance and poor performance with low-quality propensity scores and heavy-tailed reward distributions.
method Introduces a novel estimator based on the log-sum-exponential (LSE) operator.
result Achieves convergence rate of O(n−ε/(1+ε)) for regret bounds. 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 …
Proposes MR estimator for unbiased recommendation models.
problem Data biases in recommender systems lead to inaccurate predictions.
method Introduces multiple robust (MR) learning approach combining multiple imputation and propensity models.
result MR estimator achieves unbiasedness when any of the models is accurate.
Quantum neural networks improve causal inference in biomedical studies, especially for small samples.
problem Addressing selection bias in comparing surgical techniques using observational data.
method Developed QNN-based propensity score models focusing on four key covariates (Age, Sex, Stage, BMI). Employed a linear ZFeatureMap for data encoding, SummedPaulis for predictions, and CMA-ES for optimization. Integrated noise modeling to enhance predictive stability.
result QNNs, particularly with noise-aware strategies, outperformed classical models in small samples, achieving AUC up to 0.750 for n=100.
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.
Optimizes insurance pricing by accounting for policyholders' price sensitivity.
problem Traditional insurance pricing does not consider policyholders' price sensitivity.
method Formulates insurance pricing as a decision-making problem and uses off-policy evaluation and stochastic control.
result Neural networks outperform existing techniques for policy optimization.
New method estimates consumer surplus from randomized pricing data.
problem Estimating consumer surplus from observational data, especially in AI-driven pricing.
method Cumulative Propensity Weights (CPW) and Augmented CPW (ACPW) estimators.
result Validated methods for estimating consumer surplus from randomized pricing data.
In epidemiology, identifying the effect of exposure variables in relation to a time-to-event outcome is a classical research area of practical importance. Incorporating propensity score in the Cox regression model, as a measure to control for confounding, has certain advantages when outcome is rare. However, in situati…
Pessimistic estimator improves multi-objective policy optimization.
problem Optimizing multi-objective policies from existing data.
method Pessimistic estimator based on inverse propensity scores (IPS).
result Pessimistic estimator outperforms naive IPS estimator in theory and experiments.
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.
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.
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…
Estimating average causal effect (ACE) is useful whenever we want to know the effect of an intervention on a given outcome. In the absence of a randomized experiment, many methods such as stratification and inverse propensity weighting have been proposed to estimate ACE. However, it is hard to know which method is opti…
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.
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.
The paper addresses statistical inference for online decision-making in a contextual bandit setting.
problem Understanding the performance of reward models in online decision-making with contextual information.
method The paper uses the contextual bandit framework with a linear reward model and the ε-greedy policy to address the exploration-exploitation dilemma. It employs the martingale central limit theorem and inverse propensity score weighting to establish asymptotic normality of parameter estimators. result The online ordinary least squares estimator and the online weighted least squares estimator are asymptotically normal, providing insights into the performance of the reward model.
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.
Study improves unbiased recommender learning by addressing missing-reward bias.
problem Data bias caused by missing-reward observations in recommender systems.
method Proposes a novel estimator using propensity scores to mitigate both position and reward bias.
result The proposed estimator outperforms other methods, even with increased reward observation bias.
The positivity assumption, or the experimental treatment assignment (ETA) assumption, is important for identifiability in causal inference. Even if the positivity assumption holds, practical violations of this assumption may jeopardize the finite sample performance of the causal estimator. One of the consequences of pr…
Proposes a method to improve learning when training data is not representative.
problem Improving supervised learning when training data is not representative (covariate shift).
method Conditioning on propensity scores to balance covariates within strata.
result Significantly improved target prediction and AUC (0.958) on supernovae classification challenge.
Proposes a new method to handle data heterogeneity in causal inference.
problem Challenges of collaborating between different data centers due to heterogeneity.
method Collaborative inverse propensity score weighting estimator to adjust for distribution shift.
result Significant improvements over traditional meta-analysis methods when dealing with increased heterogeneity.
Data mining and machine learning techniques such as classification and regression trees (CART) represent a promising alternative to conventional logistic regression for propensity score estimation. Whereas incomplete data preclude the fitting of a logistic regression on all subjects, CART is appealing in part because s…
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…