6 papers
A Design-Based Minimax Theory for Network Experiments
Vardis Kandiros, Christopher Harshaw, Fredrik Sävje
Network experiments are used throughout the social and medical sciences to investigate causal effects under the presence of interference. While a large body of work has developed i…
On the Impossibility of Specification Testing of Interference Models Based on Exposure Mappings
Chao Gao, Christopher Harshaw, Fredrik Sävje +1
Researchers use interference models based on exposure mappings to facilitate estimation of causal effects in randomized experiments with interference. To test the veracity of such…
Valid Inference when Testing Violations of Parallel Trends for Difference-in-Differences
Jonas M. Mikhaeil, Christopher Harshaw
The difference-in-differences (DID) research design is a key identification strategy which allows researchers to estimate causal effects under the parallel trends assumption. While…
Sigmoid-FTRL: Design-Based Adaptive Neyman Allocation for AIPW Estimators
Fangyi Chen, Shu Ge, Jian Qian +1
We consider the problem of Adaptive Neyman Allocation for the class of AIPW estimators in a design-based setting, where potential outcomes and covariates are deterministic. As each…
The Conflict Graph Design: Estimating Causal Effects under Arbitrary Neighborhood Interference
Vardis Kandiros, Charilaos Pipis, Constantinos Daskalakis +1
A fundamental problem in network experiments is selecting an appropriate experimental design in order to precisely estimate a given causal effect of interest. In this work, we prop…
A General Design-Based Framework and Estimator for Randomized Experiments
Christopher Harshaw, Fredrik Sävje, Yitan Wang
We describe a design-based framework for drawing causal inference in general randomized experiments. Causal effects are defined as linear functionals evaluated at unit-level potent…