3 citations · 8 across the 6 of their papers we have counts for
6 papers
Discovering Optimal Scoring Mechanisms in Causal Strategic Prediction
Tom Yan, Shantanu Gupta, Zachary Lipton
Faced with data-driven policies, individuals will manipulate their features to obtain favorable decisions. While earlier works cast these manipulations as undesirable gaming, recen…
Local Causal Discovery for Estimating Causal Effects
Shantanu Gupta, David Childers, Zachary C. Lipton
Even when the causal graph underlying our data is unknown, we can use observational data to narrow down the possible values that an average treatment effect (ATE) can take by (1) i…
Homophily and Incentive Effects in Use of Algorithms
Riccardo Fogliato, Sina Fazelpour, Shantanu Gupta +2
As algorithmic tools increasingly aid experts in making consequential decisions, the need to understand the precise factors that mediate their influence has grown commensurately. I…
Efficient Online Estimation of Causal Effects by Deciding What to Observe
Shantanu Gupta, Zachary C. Lipton, David Childers
Researchers often face data fusion problems, where multiple data sources are available, each capturing a distinct subset of variables. While problem formulations typically take the…
Correcting Exposure Bias for Link Recommendation
Shantanu Gupta, Hao Wang, Zachary C. Lipton +1
Link prediction methods are frequently applied in recommender systems, e.g., to suggest citations for academic papers or friends in social networks. However, exposure bias can aris…
Estimating Treatment Effects with Observed Confounders and Mediators
Shantanu Gupta, Zachary C. Lipton, David Childers
Given a causal graph, the do-calculus can express treatment effects as functionals of the observational joint distribution that can be estimated empirically. Sometimes the do-calcu…