1 citations · 1 across the 4 of their papers we have counts for
5 papers
Double Variable Importance Matching to Estimate Distinct Causal Effects on Event Probability and Timing
Yuqi Li, Quinn Lanners, Matthew M. Engelhard
In many clinical contexts, estimating effects of treatment in time-to-event data is complicated not only by confounding, censoring, and heterogeneity, but also by the presence of a…
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…
Counterfactual Evaluation of Ads Ranking Models through Domain Adaptation
Mohamed A. Radwan, Himaghna Bhattacharjee, Quinn Lanners +4
We propose a domain-adapted reward model that works alongside an Offline A/B testing system for evaluating ranking models. This approach effectively measures reward for ranking mod…
Safe and Interpretable Estimation of Optimal Treatment Regimes
Harsh Parikh, Quinn Lanners, Zade Akras +4
Recent statistical and reinforcement learning methods have significantly advanced patient care strategies. However, these approaches face substantial challenges in high-stakes cont…
Variable Importance Matching for Causal Inference
Quinn Lanners, Harsh Parikh, Alexander Volfovsky +2
Our goal is to produce methods for observational causal inference that are auditable, easy to troubleshoot, accurate for treatment effect estimation, and scalable to high-dimension…