Discusses handling intercurrent events in clinical trials with time-to-event outcomes.
problem Handling intercurrent events in clinical trials with time-to-event outcomes.
method Defines estimands and six ICE handling strategies, including new competing-risk strategy.
result Novel methods for handling intercurrent events in clinical trials with time-to-event outcomes.
Unified framework for automatic debiased machine learning for various statistical parameters.
problem Inference on smooth functionals of nonparametric M-estimands.
method Unified framework using gradient, Hessian, and linear approximation; solves two risk minimization problems.
result Efficient autoDML estimators with double robustness and robustness to misspecification.
New approach to off-policy evaluation connects causal graph to policy effects.
problem Evaluating policies using observational data from different policies.
method Formalizes off-policy evaluation within a causal graph framework.
result Identifies specific causal estimands and highlights necessary experimental data.
This study quantifies uncertainty in comparing treatments using RCTs with before-and-after measures.
problem Uncertainty in comparing treatments using RCTs with before-and-after measures.
method New statistical modeling principle called ETZ enables counterfactual uncertainty quantification (CUQ) in RCTs with Before-and-After Repeated Measures.
result CUQ typically has lower variability than factual uncertainty quantification and can be achieved in RCTs.
In causal inference, a variety of causal effect estimands have been studied, including the sample, uncensored, target, conditional, optimal subpopulation, and optimal weighted average treatment effects. Ad-hoc methods have been developed for each estimand based on inverse probability weighting (IPW) and on outcome regr…
Prognostic scores improve logistic regression analysis in RCTs with binary outcomes.
problem Non-collapsibility in logistic regression analysis of RCTs with binary endpoints.
method Prognostic score adjustment using AI predictions to address non-collapsibility.
result Prognostic score adjustment increases power or reduces sample size for estimating conditional odds ratios.
Proposes new methods for inference in GLMs without assuming model correctness.
problem Inference for GLMs assumes model correctness, leading to uncertainty and bias.
method Develops nonparametric estimands and uses influence curves with flexible procedures.
result Inference for GLM parameters is improved without model correctness assumptions.
Develops new instance-optimality concepts in differential privacy.
problem Improving privacy guarantees in statistical estimation.
method Introduces local minimax risk and unbiased mechanisms, and develops inverse sensitivity mechanisms.
result Inverse sensitivity mechanisms are nearly instance optimal for a wide range of functions.
New framework estimates treatment effects based on preferences.
problem Estimating treatment effects with flexible outcomes.
method Preference-based Conditional Treatment Effect (CPTE) framework.
result CPTE provides interpretable targets and new identifiability conditions.
Generalizes causal inference to high-dimensional outcomes.
problem Limited causal inference methods for multivariate outcomes.
method Formulates causal discrepancy tests for nominal variables, uses conditional independence tests.
result Causal CDcorr method improves finite sample validity and power.
A method for finding most influential sets reduces a complex problem to a sequence of simpler top- k k k problems.
problem Identifying most influential subsets in complex models.
method Reduces the problem to a sequence of top- k k k problems using Dinkelbach's method. result The method returns a globally optimal set for the univariate ratio objective, including partial linear models.
A-TMLE estimates ATE from RCT and RWD, achieving super-efficiency.
problem Estimating ATE from RCT and RWD data.
method Adaptive-TMLE framework for decomposing and estimating ATE.
result A-TMLE is root-n consistent and asymptotically normal, achieving super-efficiency.
Develops a method for causal inference in recurrent event data with terminal failure.
problem Causal inference in recurrent event data with a terminal event.
method Multiply robust estimation framework for causal inference.
result Proposes an estimator for the expected number of recurrent events and failure survival function.
The US Census Bureau corrupts data to protect privacy, but we show how to clean and analyze it effectively.
problem Analyzing Census data with intentional corruption to maintain privacy.
method Formulated a semiparametric model, proposed data cleaning, estimation, and inference procedures.
result Demonstrated that data cleaning can maintain precision and provided theoretical and empirical support.
A new method removes biases in data integration by using surrogate control outcomes.
problem Data integration methods can be biased due to data-dependent processes.
method Post-integrated inference method using surrogate control outcomes to account for latent heterogeneity.
result The method provides consistent and efficient estimators under minimal assumptions and potential misspecifications.
Unified approach for estimating quantiles of potential outcomes using inverse estimating equations.
problem Estimating quantiles of potential outcomes for causal inference.
method Inverse estimating equations and moment function.
result Unified approach to estimate mean and quantiles of potential outcomes.
Estimates treatment effects in bipartite systems with partial eligibility and interference.
problem Randomized experiments in bipartite systems with partial treatment eligibility and interference.
method Formalizes eligibility-constrained bipartite experiments, defines PTTE and STTE, identifies conditions, develops ensemble estimators, introduces projection.
result Proposed estimators recover PTTE and STTE with low bias and variance, corrects interference bias in field experiments.
New method improves robustness of neural network-based debiasing.
problem Improving robustness of neural network-based debiasing.
method Moment-constrained learning for neural networks.
result Improved performance compared to state-of-the-art benchmarks.
Decomposes spillover effects under misspecified exposure mappings.
problem Modeling outcomes as functions of own treatment and a misspecified exposure mapping of others' treatments.
method Pseudo-true estimands and local-global extension for structured misspecification.
result Sharp asymptotic decomposition into direct, local, and global components.
Scientists develop a model to identify treatment responders from non-responders.
problem Analyzing samples that respond to treatment in studies.
method Causal two-groups (C2G) model, empirical Bayes procedures.
result The C2G model controls false discovery rate and has near-optimal power.
Meta-learning method for estimating time-varying mHealth intervention effects.
problem Complex mHealth data and uncertain randomization probabilities.
method DR-WCLS meta-learning procedure for causal excursion effects.
result More efficient and consistent estimates of causal excursion effects.
Study finds no significant short-term impact on liquidity supply after protocol fees were reduced.
problem Liquidity provider welfare is affected by protocol fees, but the impact on liquidity supply is unclear.
method Used a matched-overlap event-study difference-in-differences design to estimate the liquidity-supply response to take-rate cuts.
result No significant short-term impact on active liquidity or local depth; no change in LP participation or composition.
Unified framework for estimating indirect effects in observational studies with unmeasured confounding.
problem Challenges in evaluating indirect effects due to unmeasured confounding and unethical exposures.
method Developed a unified identification and estimation framework using proximal causal inference.
result Unified identification and estimation of PIIE and causal effect of an intervening variable in settings with pervasive unmeasured confounding.
Proposes a Bayesian framework for causal inference without explicit likelihood modeling.
problem Challenges in principled Bayesian inference for causal effects.
method Generalized Bayesian framework that places priors directly on causal estimands and updates using identification-driven loss functions.
result Yields generalized posteriors for causal effects with uncertainty quantification.
Directly estimates CQC, improving interpretability and accuracy.
problem Inability to model and interpret CQC due to inversion issue.
method Direct doubly robust estimation of CQC without inversion.
result Improved estimation accuracy and interpretability.
New method resolves causal heterogeneity by defining a resolution profile.
problem Causal subgroup analyses often oversimplify heterogeneity into a small number of groups.
method Introduces a resolution profile as a functional of the causal feature law, using Bayesian-bootstrap inference.
result Shows that the resolution profile is a continuous path with discontinuities at knots, providing integer-valued subgroup numbers.
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.
New method corrects bias in missing data for matrix completion.
problem Missing data bias in matrix completion.
method Causal model and synthetic nearest neighbors (SNN) method.
result Synthetic nearest neighbors (SNN) method provides consistent and normal estimates.
PPI uses survey sampling methods for inference, bridging ML and statistics.
problem Combining machine learning predictions with small labeled data for valid inference.
method Equivalence of PPI estimators to survey sampling methods.
result PPI estimators are algebraically equivalent to survey sampling methods.
Unified method for inference on partially identified causal effects using covariates.
problem Partial identification of causal effects due to unobserved joint potential outcomes.
method Model-agnostic approach using duality theory for optimal transport problems.
result Uniformly valid inference for a wide class of estimands, even with inaccurate nuisance parameter estimates.
Framework identifies population quantities from MNAR feedback using weak shadow variables from pretrained models.
problem Estimating mean outcomes from MNAR user feedback with bias and lack of identification.
method Develops a partial identification framework using linear programs and weak shadow variables from pretrained models.
result Bounds on estimand are obtained by solving linear programs incorporating pretrained model predictions.
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.
Develops a TL framework for estimating RMST difference in clinical trials.
problem Estimating RMST difference in clinical trials with time-to-event outcomes.
method Targeted learning (TL) framework using pseudo-observations and copy reference (CR) approach for sensitivity analysis.
result Demonstrated the effectiveness of the TL framework using real data.
The paper introduces Shapley curves for measuring variable importance in nonparametric settings.
problem Limited statistical understanding of Shapley values as variable importance measures.
method Introduces Shapley curves based on conditional expectation and covariate distribution; derives convergence rates and normality; proposes a novel bootstrap procedure.
result Validates theoretical findings with numerical studies and analyzes vehicle prices determinants.
Paper develops robust econometric methods for staggered adoption studies.
problem Estimation challenges in event studies with staggered adoption.
method Design-first framework with exact probability limits, diagnostics, and orthogonal score constructions.
result Uniformly valid inference under restricted violations of parallel trends.
New method for debiased inference without assuming exact solutions in inverse problems.
problem Dealing with inverse problems where exact solutions may not exist.
method Nonparametric instrumental variable analysis without structural equations.
result Valid inference on functionals of inverse problems without assuming exact solutions.
TabMGP uses a martingale posterior with TabPFN to estimate uncertainty in tabular data.
problem Estimating uncertainty in models like TabPFN for scientific estimands θ θ θ . method TabMGP is an MGP built on TabPFN, focusing inference on loss function-defined parameters.
result TabMGP produces credible sets with near-nominal coverage and often outperforms baselines.
Estimates and tests treatment effects on entire outcome distributions.
problem Treatment effects on entire outcome distributions, not just averages.
method Proposes a novel estimand and doubly robust estimator, develops a test.
result First test with provably valid type 1 error guarantees in this setting.
New framework evaluates model explanations based on decision task improvement.
problem Evaluation of model explanations often misses practical value.
method Decision-theoretic framework quantifying three key values.
result Provides benchmarks and interprets human-AI decision support.
New GP-based method improves uncertainty quantification for causal functions.
problem Challenges in quantifying uncertainty for causal effects, especially for entire functions.
method GP-based approach using inner-product of observational functions in RKHS, with tractable posterior moments and calibration.
result Improves uncertainty quantification while maintaining causal effect estimation performance.
The paper addresses causal mediation analysis with post-treatment events, proposing robust estimators and efficient methods.
problem Assessing causal mediation in the presence of post-treatment events like noncompliance or clinical events.
method Identifies natural mediation effects for entire populations and principal strata, derives efficient influence functions, and proposes multiply robust estimators.
result Multiply robust estimators are consistent under four types of misspecifications and efficient when all models are correct.
Paper develops methods for estimating GLMs and SNR under proportional asymptotics.
problem Estimation of regression coefficients and SNR in high-dimensional GLMs.
method Method-of-Moments type estimators that bypass nuisance function estimation.
result Consistent and asymptotically normal estimators derived for targets of inference.
The paper corrects for node degree in spectral clustering using random walk Laplacian.
problem Node degree heterogeneity in spectral clustering.
method Graph spectral embedding using the random walk Laplacian.
result The embedding provides uniformly consistent estimates of degree-corrected latent positions.
Method constructs prediction intervals for time-varying individual treatment effects.
problem Accurately quantify uncertainty of individual treatment effects across multiple decision points.
method Conformal inference techniques for time-varying ITEs with weaker assumptions.
result Guaranteed lower bound for coverage dependent on data non-exchangeability.
E-QRGMM accelerates uncertainty quantification in simulations.
problem Challenges in covariate-dependent uncertainty quantification.
method Integrates cubic Hermite interpolation with gradient estimation.
result Substantially improves computational efficiency and accuracy.
Estimates outcomes under hypothetical scenarios using a flexible framework.
problem Adapting to sudden shifts in treatment patterns.
method Doubly robust estimator using incremental interventions.
result Achieves n \sqrt{n} n -consistency and asymptotic normality. Researchers formalize PD and PFI to relate them to data generating process.
problem Lack of theory linking PD and PFI to data generating process.
method Formalize PD and PFI as estimators of ground truth estimands, account for model variance with learner-PD and learner-PFI.
result PD and PFI estimates deviate from ground truth due to statistical biases, model variance, and Monte Carlo approximation errors.
Generalized Chinese Remainder Theorem (CRT) has been shown to be a powerful approach to solve the ambiguity resolution problem. However, with its close relationship to number theory, study in this area is mainly from a coding theory perspective under deterministic conditions. Nevertheless, it can be proved that even wi…