JSM2025
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Contributed Papers

Methods for Causal Discovery and Causal Inference

Tue, Aug 5, 2:00 PM - 3:50 PM Room CC-106C Music City Center
Salvador BalkusChair
Section on Statistical Learning and Data Science

7 Presentations

An AI-powered Bayesian generative modeling approach for causal inference in observational studies
2:05 PM - 2:20 PM
Co-authors: Qiao Liu (Stanford University), Wing-Hung Wong (Stanford University), Qiao Liu (Stanford University)
Treatment EffectBayesian Deep LearningMarkov chain Monte CarloDose-response FunctionPotential OutcomeUncertainty Quantification
Causal Association between Child Opportunity Index (COI) and A1C levels in Type II Diabetes Patients
2:20 PM - 2:35 PM
Co-authors: Sima Sharghi (Akron Children's Hospital), Lisa Shauver (Akron Children's Hospital), Sima Sharghi (Akron Children's Hospital), Kevin Stoll (Welltower), Michael Forbes (Akron Children Hospital), Michael Oravec (Akron Children Hospital), Jonathan Pelletier (Akron Children Hospital), Ryan Heksch (Akron Children Hospital)
Causal InfrenceHealth DisparitiesSocial Determinant of HealthPublic DataChild Opportunity IndexTye 2 Diabetes
Causal inference with heavily skewed continuous variables in Google Cloud
2:35 PM - 2:50 PM
Co-authors: Xueqi Zhao, Xueqi Zhao, Tianhong He
Causal inferenceDoubly robustMachine learningContinuous variablesSkewed variablesCovariate balance
Causal Invariance Learning via Efficient Optimization of a Nonconvex Objective
2:50 PM - 3:05 PM
Co-authors: Zhenyu Wang (Rutgers University), Zhenyu Wang (Rutgers University), Yifan Hu (College of Management of Technology, EPFL), Peter Bühlmann (ETH Zurich), Zijian Guo (Rutgers University)
Causal invariance learningNonconvex optimizationComputationally efficient causal discoveryMulti-source data
Consistent DAG selection for Bayesian Causal Discovery under general error distributions
3:05 PM - 3:20 PM
Co-authors: Anamitra Chaudhuri (Department of Statistics, Texas A&M University), Anamitra Chaudhuri (Department of Statistics, Texas A&M University), Anirban Bhattacharya (Texas A&M University), Yang Ni (Texas A&M University)
Causal discoveryCausal inferenceStructural equation modelingBayesian model selectionPosterior consistencyCausal structure learning
Generalized Criterion for Identifiability of Additive Noise Models Using Majorization
3:20 PM - 3:35 PM
Co-authors: Aramayis Dallakyan, Aramayis Dallakyan, Yang Ni (Texas A&M University)
Directed Acyclic GraphsIdentifiabilityMajorizationCausal structure learning
The Recursive Partitioning BLUP (RP-BLUP) for Improved Estimation of Heterogeneous Treatment Effects
3:35 PM - 3:50 PM
Co-authors: Eunsan Kim (University of Minnesota), J. Sunil Rao, Eunsan Kim (University of Minnesota), Jiming Jiang (University of California, Davis)
Heterogeneous treatment effectRandom effectClassification and regression treesModel misspecification
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