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Contributed Papers

Recent Developments in Causal Inference

Wed, Aug 9, 10:30 AM

Navonil DebChair

Section on Statistical Learning and Data Science

6 Presentations

Sequential Designs for Causal Inference

10:35 AM - 10:50 AM

Co-authors: Simon Mak, Chien-Fu Wu (Georgia Institute of Technology)

Experimental Design Under Interference with Partial Graph Data

10:50 AM - 11:05 AM

Steven Wilkins-Reeves

Co-authors: Shane Lubold (University of Washington), Arun Chandrasekhar (Stanford University), Tyler McCormick (University of Washington)

Estimating network-mediated causal effects via spectral embeddings

11:05 AM - 11:20 AM

Alexander Hayes (University of Wisconsin-Madison)

Co-authors: Mark Fredrickson (University of Michigan), Keith Levin (University of Wisconsin)

Estimating Causal Peer Effects Using Latent Locations for Unmeasured Confounding in Social Networks

11:20 AM - 11:35 AM

Seungha Um (New York University School of Medicine)

Co-authors: Samrachana Adhikari (NYU School of Medicine)

A Distributed Semi-parametric Model to Quantify Heterogeneous Treatment Effect

11:35 AM - 11:50 AM

Co-authors: Jiayi Tong, Yang Ning (Cornell University), Yong Chen (University of Pennsylvania, Perelman School of Medicine)

WITHDRAWN Estimating Causal Effects with Interaction Screening and Selection in High-Dimensional Data

11:50 AM - 12:05 PM

Co-authors: Chinenye Ifebirinachi (North Carolina A & T State University), Yufeng Liu (University of North Carolina at Chapel Hill), Yongli Zhang (Lyft), Seong-Tae Kim (NC A&T State University)

Session participants

Cheryl Brooks (AT&T Data Science and AI Research)

Participant

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