7 papers · 1 filter
The Confounder Trap: Treatment-Encoding Representations in Causal Inference with Text
Marie Neubrander, Graham Tierney, Alexander Volfovsky
Estimating causal effects of linguistic properties from observational text is difficult because the same document can contain both the treatment of interest and the non-treatment t…
TEA-Time: Transporting Effects Across Time
Harsh Parikh, Gabriel Levin-Konigsberg, Dominique Perrault-Joncas +1
Treatment effects estimated from a randomized controlled trial are local not only to the study population but also to the time at which the trial was conducted. The literature on g…
Optimal Data Integration and Adaptive Sampling for Efficient Treatment Effect Estimation
Yen-Chun Liu, Alexander Volfovsky, German Schnaidt +2
This study addresses the challenge of estimating average treatment effects (ATEs) for advertising campaigns in online marketplaces where complete randomized experimentation is infe…
A Double Machine Learning Approach to Combining Experimental and Observational Data
Harsh Parikh, Marco Morucci, Vittorio Orlandi +3
Experimental and observational studies often lack validity due to untestable assumptions. We propose a double machine learning approach to combine experimental and observational st…
A Design-based Solution for Causal Inference with Text: Can a Language Model Be Too Large?
Graham Tierney, Srikar Katta, Christopher Bail +2
Many social science questions ask how linguistic properties causally affect an audience's attitudes and behaviors. Because text properties are often interlinked (e.g., angry review…
Data Fusion for Partial Identification of Causal Effects
Quinn Lanners, Cynthia Rudin, Alexander Volfovsky +1
Data fusion techniques integrate information from heterogeneous data sources to improve learning, generalization, and decision making across data sciences. In causal inference, the…