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Contributed Papers

Causal Inferences and Instrumental Variable Methods

Wed, Aug 6, 2:00 PM - 3:50 PM Room CC-101A Music City Center

Haitao ChuChair

Section on Statistics in Epidemiology

6 Presentations

A Unified Framework for Causal Estimand Selection

2:05 PM - 2:20 PM

Co-authors: Martha Barnard, Martha Barnard, Jared Huling (University of Minnesota), Julian Wolfson (University of Minnesota)

average treatment effect causal inference inverse probability weighting propensity score target population

Best-subset instrumental variable selection method using mixed integer optimization with application

2:20 PM - 2:35 PM

Co-authors: Muhammad Qasim (Lund university), Kristofer Månsson (Jönköping University), Muhammad Qasim (Lund university), Narayanaswamy Balakrishnan (McMaster University)

Causal inference Instrumental variables Mendelian randomization Best-subset selection Mixed integer programming Variable selection

Estimating Causal Treatment Effects in Placebo-Controlled Trials with High Placebo Response

2:35 PM - 2:50 PM

Co-authors: Yang Song (Boston University), Gheorghe Doros (Boston University), Yang Song (Boston University)

Placebo Response Causal Treatment Effect Placebo-controlled Randomized Clinical Trials (RCTs)Conditional Average Treatment Effect (CATE)Prognostic Score

Valid Instrumental Variable Selection Using Lasso under the Control Function Approach

2:50 PM - 3:05 PM

Co-authors: Jiachen Liu (Michigan State University), Yuehua Cui (Michigan State University), Jiachen Liu (Michigan State University), Haolei Weng (Michigan State University), William Schmidt (Michigan State University)

Lasso instrumental variable the control function approach multiple endogenous variables heterogenous treatment effect

The synthetic instrument: From sparse association to sparse causation

3:05 PM - 3:20 PM

Co-authors: Dingke Tang, Dingke Tang, Dehan Kong (University of Toronto), Linbo Wang (University of Toronto)

Causal inference Multivariate analysis Unmeasured confounding.

Trying unpreferred treatments for efficient causal inference

3:20 PM - 3:35 PM

Co-authors: Brian Knaeble (Utah Valley University), Brian Knaeble (Utah Valley University), Erich Kummerfeld (University of Minnesota)

Experimental control Randomization Causal inference Treatment preferences Data fusion Reinforcement learning

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