48 citations · 119 across the 13 of their papers we have counts for
15 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…
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…
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…
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…
Clip-OGD: An Experimental Design for Adaptive Neyman Allocation in Sequential Experiments
Jessica Dai, Paula Gradu, Christopher Harshaw
From clinical development of cancer therapies to investigations into partisan bias, adaptive sequential designs have become increasingly popular method for causal inference, as the…
A General Design-Based Framework and Estimator for Randomized Experiments
Christopher Harshaw, Fredrik Sävje, Yitan Wang
We describe a widely applicable design-based framework for drawing causal inference in randomized experiments. Causal effects are defined as linear functionals evaluated at unit-le…
Optimized variance estimation under interference and complex experimental designs
Christopher Harshaw, Joel A. Middleton, Fredrik Sävje
Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome…
The Power of Subsampling in Submodular Maximization
Christopher Harshaw, Ehsan Kazemi, Moran Feldman +1
We propose subsampling as a unified algorithmic technique for submodular maximization in centralized and online settings. The idea is simple: independently sample elements from the…
Design and Analysis of Bipartite Experiments under a Linear Exposure-Response Model
Christopher Harshaw, Fredrik Sävje, David Eisenstat +2
A bipartite experiment consists of one set of units being assigned treatments and another set of units for which we measure outcomes. The two sets of units are connected by a bipar…
How Do You Want Your Greedy: Simultaneous or Repeated?
Moran Feldman, Christopher Harshaw, Amin Karbasi
We present SimultaneousGreedys, a deterministic algorithm for constrained submodular maximization. At a high level, the algorithm maintains solutions and greedily updates th…
Balancing Covariates in Randomized Experiments with the Gram-Schmidt Walk Design
Christopher Harshaw, Fredrik Sävje, Daniel Spielman +1
The design of experiments involves a compromise between covariate balance and robustness. This paper provides a formalization of this trade-off and describes an experimental design…
Submodular Maximization Beyond Non-negativity: Guarantees, Fast Algorithms, and Applications
Christopher Harshaw, Moran Feldman, Justin Ward +1
It is generally believed that submodular functions -- and the more general class of -weakly submodular functions -- may only be optimized under the non-negativity assumption $f(…
Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity
Lin Chen, Christopher Harshaw, Hamed Hassani +1
Online optimization has been a successful framework for solving large-scale problems under computational constraints and partial information. Current methods for online convex opti…
Greed is Good: Near-Optimal Submodular Maximization via Greedy Optimization
Moran Feldman, Christopher Harshaw, Amin Karbasi
It is known that greedy methods perform well for maximizing monotone submodular functions. At the same time, such methods perform poorly in the face of non-monotonicity. In this pa…