14 citations · 82 across the 28 of their papers we have counts for
29 papers
Dynamic Treatment on Networks
Bengusu Nar, Jiguang Li, Veronika Ročková +1
In networks, effective dynamic treatment allocation requires deciding both whom to treat and also when, so as to amplify policy impact through spillovers. An early intervention at…
Permutation Inference under Multi-way Clustering and Missing Data
Wenxuan Guo, Panos Toulis, Yuhao Wang
Econometric applications with multi-way clustering often feature a small number of effective clusters or heavy-tailed data, making standard cluster-robust and bootstrap inference u…
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion
Hwanwoo Kim, Panos Toulis, Eric Laber
Temporal difference (TD) learning is a foundational algorithm in reinforcement learning (RL). For nearly forty years, TD learning has served as a workhorse for applied RL as well a…
Randomization Inference in Two-Sided Market Experiments
Jizhou Liu, Azeem M. Shaikh, Panos Toulis
Randomized experiments are increasingly employed in two-sided markets, such as buyer--seller platforms, to evaluate the effects of marketplace interventions. These experiments must…
Finite-Sample Valid Randomization Tests for Monotone Spillover Effects
Shunzhuang Huang, Xinran Li, Panos Toulis
Randomization tests have gained popularity for causal inference under network interference because they are finite-sample valid with minimal assumptions. However, existing procedur…
Gaussianized Design Optimization for Covariate Balance in Randomized Experiments
Wenxuan Guo, Tengyuan Liang, Panos Toulis
Achieving covariate balance in randomized experiments enhances the precision of treatment effect estimation. However, existing methods often require heuristic adjustments based on…
ML-assisted Randomization Tests for Detecting Treatment Effects in A/B Experiments
Wenxuan Guo, JungHo Lee, Panos Toulis
Experimentation is widely utilized for causal inference and data-driven decision-making across disciplines. In an A/B experiment, for example, an online business randomizes two dif…
Invariance-based Inference in High-Dimensional Regression with Finite-Sample Guarantees
Wenxuan Guo, Panos Toulis
In this paper, we develop invariance-based procedures for testing and inference in high-dimensional regression models. These procedures, also known as randomization tests, provide…
Convergence and Stability of the Stochastic Proximal Point Algorithm with Momentum
Junhyung Lyle Kim, Panos Toulis, Anastasios Kyrillidis
Stochastic gradient descent with momentum (SGDM) is the dominant algorithm in many optimization scenarios, including convex optimization instances and non-convex neural network tra…
Robust Inference for High-Dimensional Linear Models via Residual Randomization
Y. Samuel Wang, Si Kai Lee, Panos Toulis +1
We propose a residual randomization procedure designed for robust Lasso-based inference in the high-dimensional setting. Compared to earlier work that focuses on sub-Gaussian error…
Randomization Inference of Periodicity in Unequally Spaced Time Series with Application to Exoplanet Detection
Panos Toulis, Jacob Bean
The estimation of periodicity is a fundamental task in many scientific areas of study. Existing methods rely on theoretical assumptions that the observation times have equal or i.i…
Estimation of Covid-19 Prevalence from Serology Tests: A Partial Identification Approach
Panos Toulis
We propose a partial identification method for estimating disease prevalence from serology studies. Our data are results from antibody tests in some population sample, where the te…
Randomization Tests in Observational Studies with Staggered Adoption of Treatment
Azeem Shaikh, Panos Toulis
This paper considers the problem of inference in observational studies with time-varying adoption of treatment. In addition to an unconfoundedness assumption that the potential out…
A Graph-Theoretic Approach to Randomization Tests of Causal Effects Under General Interference
David Puelz, Guillaume Basse, Avi Feller +1
Interference exists when a unit's outcome depends on another unit's treatment assignment. For example, intensive policing on one street could have a spillover effect on neighboring…
Asymptotic Validity and Finite-Sample Properties of Approximate Randomization Tests
Panos Toulis
Randomization tests rely on simple data transformations and possess an appealing robustness property. In addition to being finite-sample valid if the data distribution is invariant…
Minimax designs for causal effects in temporal experiments with treatment habituation
Guillaume Basse, Yi Ding, Panos Toulis
Randomized experiments are the gold standard for estimating the causal effects of an intervention. In the simplest setting, each experimental unit is randomly assigned to receive t…
Randomization tests for peer effects in group formation experiments
Guillaume Basse, Peng Ding, Avi Feller +1
Measuring the effect of peers on individuals' outcomes is a challenging problem, in part because individuals often select peers who are similar in both observable and unobservable…
Dynamical systems theory for causal inference with application to synthetic control methods
Yi Ding, Panos Toulis
In this paper, we adopt results in nonlinear time series analysis for causal inference in dynamical settings.~Our motivation is policy analysis with panel data, particularly throug…
Propensity score methodology in the presence of network entanglement between treatments
Panos Toulis, Alexander Volfovsky, Edoardo M. Airoldi
In experimental design and causal inference, it may happen that the treatment is not defined on individual experimental units, but rather on pairs or, more generally, on groups of…
Convergence diagnostics for stochastic gradient descent with constant step size
Jerry Chee, Panos Toulis
Many iterative procedures in stochastic optimization exhibit a transient phase followed by a stationary phase. During the transient phase the procedure converges towards a region o…
Conditional randomization tests of causal effects with interference between units
Guillaume Basse, Avi Feller, Panos Toulis
Many causal questions involve interactions between units, also known as interference, for example between individuals in households, students in schools, or firms in markets. In th…
The Proximal Robbins-Monro Method
Panos Toulis, Thibaut Horel, Edoardo M. Airoldi
The need for parameter estimation with massive datasets has reinvigorated interest in stochastic optimization and iterative estimation procedures. Stochastic approximations are at…
Stochastic gradient descent methods for estimation with large data sets
Dustin Tran, Panos Toulis, Edoardo M. Airoldi
We develop methods for parameter estimation in settings with large-scale data sets, where traditional methods are no longer tenable. Our methods rely on stochastic approximations,…
Incentive-Compatible Experimental Design
Panos Toulis, David C. Parkes, Elery Pfeffer +1
We consider the design of experiments to evaluate treatments that are administered by self-interested agents, each seeking to achieve the highest evaluation and win the experiment.…