Recent Talks and Posters

Combining Rollout Designs and Clustering for Causal Inference under Low-order Interference

I'll introduce a two-stage rollout design to mitigate the high variance from extrapolation in interpolation-based estimators for the total treatment effect under unknown network interference. Then, I'll quantify the bias and variance of this estimator as a function of a clustering used in the two-stage design and illustrate their trade-off through a series of experiments.

Aug 3, 2025

Combining Design and Analysis for Causal Inference under Network Interference

Interference effects, where the treatment of one individual has an effect on the outcome of another, are pervasive in real-world settings but violate assumptions of many classical causal estimators. While the Horvitz-Thompson estimator can account for interference, it has prohibitively high variance. In this talk, we'll survey recent approaches to improve on this variance guarantee by imposing additional structural assumptions on the potential outcomes model or the interference network. Then, I'll introduce a class of estimators, pseudoinverse estimators, that can be adapted to any experimental design and have strong bias and variance guarantees. For the setting of cluster randomized designs, I'll quantify the bias and variance as functions of the selected clustering.

May 26, 2025

Causal Inference under Low-Order Interference

Interference effects, where the treatment of one individual has an effect on the outcome of another, are pervasive in real-world settings but violate assumptions of many classical causal estimators. While the Horvitz-Thompson estimator can account for interference, it has prohibitively high variance. In this talk, we'll survey recent approaches to improve on this variance guarantee by imposing additional structural assumptions on the potential outcomes model or the interference network. Then, I'll introduce a class of estimators, pseudoinverse estimators, that can be adapted to any experimental design and have strong bias and variance guarantees. Finally, I'll show how theoretical bounds on the performance of the pseudoinverse estimator can provide practical advice when selecting an experimental design.

Jun 17, 2024

Low-Order Outcomes and Clustered Designs: Combining Design and Analysis for Causal Inference under Interference

Poster

May 16, 2024

Clustered Rollout Designs for Causal Inference with Network Interference

Poster

May 15, 2024

Low-Degree Outcomes and Clustered Designs: A Combined Approach for Causal Inference Under Interference

Recent work on causal inference under interference falls under two approaches, using structural assumptions on the interference effects to select a good randomized design or using structural assumptions on the potential outcomes to select a good estimator. In this work, we quantify the gains that can be made when these approaches are considered together, in particular by studying pseudoinverse estimators under cluster randomized designs.

Oct 18, 2023

Online Allocation with Priorities and Quotas

We consider the problem of online allocation, where irrevocable decisions whether to allocate to agents must be made before all agents have been observed, in settings with priority and quota constraints, By leveraging structure from an offline variant of this problem, we develop a policy for which the sum of the efficiency loss (number of unallocated resource units) and priority loss (number of higher-priority agents who are blocked from an allocation by lower-priority agents) is constant with respect to the input size.

Oct 16, 2023

To Treat or not to Treat, That is the Questions

Poster

Oct 2, 2023

Exploiting Neighborhood Interference with Low Order Interactions under Unit Randomized Design

Poster

May 25, 2023

Casual Inference with Neighborhood Interference and Low-Order Interactions

Poster

Dec 2, 2022