ACIC 2022: Room 1

Tuesday May 24, 2022
Session 1 - 9:15am-10:25am: Post-treatment complications in randomized trials

Discussant: Jason Roy

TitleAuthor
Using a Surrogate with Heterogeneous Utility to Test for a Treatment EffectParast, Layla*; Cai, Tianxi; Tian, Lu
Evaluating causal effects on time-to-event outcomes in clinical trials in the presence of treatment discontinuation due to adverse eventsBallerini, Veronica*; Bornkamp, Björn; Mattei, Alessandra; Mealli, Fabrizia F; Wang, Craig; Zhang, Yufen
Improving the precision of instrumental variable estimators with post-stratificationPashley, Nicole E.*; Miratrix, Luke; Keele, Luke J

Session 2 - 10:50am-12:00pm: Heterogeneous treatment effects

Discussant: Michael Kosorok

TitleAuthor
Posterior summarization in Bayesian causal modelingMurray, Jared S*
Minimax rates for heterogeneous Causal Effect EstimationKennedy, Edward H*; Balakrishnan, Sivaraman; Wasserman, Larry
Debiasing Random Forests for Treatment Effect EstimationSaarinen, Theo*; Yu, Bin; Sekhon, Jasjeet

Session 3 - 1:15pm-2:25pm: Causal inference in Fairness and Policy

Discussant: Luke Keele

TitleAuthors
Causal Conceptions of Fairness and their ConsequencesNilforoshan, Hamed*; Gaebler, Johann; Shroff, Ravi; Goel, Sharad
Counterfactual audit for racial bias in police traffic stopsCoston, Amanda*; Kennedy, Edward H
Safe Policy Learning through Extrapolation: Application to Pre-trial Risk AssessmentBen-Michael, Eli*; Greiner, Jim; Imai, Kosuke; Jiang, Zhichao
Session 4 - 2:50pm-4:00pm : Causal inference and COVID-19

Discussant: Theis Lange

TitleAuthor
Causal identification of infectious disease intervention effectsCrawford, Forrest W*; Cai, Xiaoxuan; Kenah, Eben
Corticosteroids in COVID-19: Optimizing Observational Research through Target Trial EmulationsHoffman, Katherine*; Diaz, Ivan
Partial Likelihood Thompson SamplingWu, Han*; Wager, Stefan

Wednesday May 25, 2022

Session 5 - 8:45am-9:55am: Applications of Causal Inference to Tech Platforms

Discussant: Jasjeet Sekhon

TitleAuthors
A framework for causal segmentation analysis with machine learning in large-scale digital experimentHejazi, Nima*; Zheng, Wenjing; Anand, Sathya
5 Things We Have Learned From Continuous Explore Exploit Applications at NetflixLiu, Sophia*; Miroglio, Ben; Sun, Ai-Lei; Lee, Ting-po
Assessing the Effectiveness of Digital Political Fundraising Ads on Facebook: Ad Delivery Algorithm, Impressions, and Ad ContentAlavi, Soogand*; Xie, Ying; Tehrani, Shervin

Session 6 - 11:15am-12:25pm: Randomized trials with Competition and Interference

Discussant: Guido Imbens

TitleAuthors
Experimental Design in Marketplaces: Competition and InterferenceJohari, Ramesh ; Li, Hannah*; Weintraub, Gabriel; Zhao, Geng
Optimized variance estimation under interference and complex experimental designsHarshaw, Chris; Middleton, Joel; Savje, Fredrik*
Network Interference in Micro-Randomized TrialsLi, Shuangning*; Wager, Stefan

Session 7 - 1:55pm-3:05pm: Causal inference with Structured Data

Discussant: Alan Hubbard

TitleAuthors
Generating Synthetic Text Data to Evaluate Causal Inference MethodsWood-Doughty, Zach*; Shpitser, Ilya; Dredze, Mark
Counterfactual Invariance: Defining and Handling Spurious Associations in Machine Learning via CausalityVeitch, Victor*; D'Amour, Alexander; Yadlowsky, Steve; Eisenstein, Jacob
Causal Matrix CompletionAgarwal, Anish*; Dahleh, Munther; Shah, Devavrat; Shen, Dennis