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01/26/2023
Proximal Causal Learning of Heterogeneous Treatment Effects
Efficiently and flexibly estimating treatment effect heterogeneity is an...
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12/21/2022
A hidden Markov modeling approach combining objective measure of activity and subjective measure of self-reported sleep to estimate the sleep-wake cycle
Characterizing the sleep-wake cycle in adolescents is an important prere...
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12/19/2022
Optimal Individualized Decision-Making with Proxies
A common concern when a policymaker draws causal inferences from and mak...
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05/11/2022
Demystifying Inferential Models: A Fiducial Perspective
Inferential models have recently gained in popularity for valid uncertai...
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04/27/2022
Proximal Causal Inference for Marginal Counterfactual Survival Curves
Contrasting marginal counterfactual survival curves across treatment arm...
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11/29/2021
Pessimistic Model Selection for Offline Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) has demonstrated great potentials in s...
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11/28/2021
A unified nonparametric fiducial approach to interval-censored data
Censored data, where the event time is partially observed, are challengi...
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10/21/2021
Individualized Decision-Making Under Partial Identification: Three Perspectives, Two Optimality Results, and One Paradox
Unmeasured confounding is a threat to causal inference and gives rise to...
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09/04/2021
Confidence Distribution and Distribution Estimation for Modern Statistical Inference
This paper introduces to readers the new concept and methodology of conf...
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03/24/2021
Dimension Reduction Forests: Local Variable Importance using Structured Random Forests
Random forests are one of the most popular machine learning methods due ...
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11/17/2020
Semiparametric proximal causal inference
Skepticism about the assumption of no unmeasured confounding, also known...
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10/07/2020
On a necessary and sufficient identification condition of optimal treatment regimes with an instrumental variable
Unmeasured confounding is a threat to causal inference and individualize...
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09/23/2020
An Introduction to Proximal Causal Learning
A standard assumption for causal inference from observational data is th...
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06/09/2020
A fiducial approach to nonparametric deconvolution problem: discrete case
Fiducial inference, as generalized by Hannig 2016 et al, is applied to n...
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04/24/2020
A Simple Weighted Approach for Instrumental Variable Estimation of Marginal Structural Mean Models
Robins 1997 introduced marginal structural models (MSMs), a general clas...
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01/27/2020
Estimating heterogeneous treatment effects with right-censored data via causal survival forests
There is fast-growing literature on estimating heterogeneous treatment e...
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11/21/2019
A semiparametric instrumental variable approach to optimal treatment regimes under endogeneity
There is a fast-growing literature on estimating optimal treatment regim...
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11/05/2019
Bias-aware model selection for machine learning of doubly robust functionals
While model selection is a well-studied topic in parametric and nonparam...
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09/14/2018
