148 citations · 972 across the 92 of their papers we have counts for
13 papers · 1 filter
Design-Based Anytime-Valid Inference for Randomized Experiments with Delayed Outcomes and Staggered Entry
Michael Lindon, Nathan Kallus
Delayed outcomes are ubiquitous in online experimentation: treatment can affect whether an outcome occurs, when it occurs, and its realized value. To accommodate staggered entry wh…
GAAVI: Global Asymptotic Anytime Valid Inference for the Conditional Mean Function
Brian M Cho, Raaz Dwivedi, Nathan Kallus
Inference on the conditional mean function (CMF) is central to tasks from adaptive experimentation to optimal treatment assignment and algorithmic fairness auditing. In this work,…
Simulation-Based Inference for Adaptive Experiments
Brian M Cho, Aurélien Bibaut, Nathan Kallus
Multi-arm bandit experimental designs are increasingly being adopted over standard randomized trials due to their potential to improve outcomes for study participants, enable faste…
Efficient Adaptive Experimentation with Noncompliance
Miruna Oprescu, Brian M Cho, Nathan Kallus
We study the problem of estimating the average treatment effect (ATE) in adaptive experiments where treatment can only be encouraged -- rather than directly assigned -- via a binar…
Estimating Heterogeneous Treatment Effects by Combining Weak Instruments and Observational Data
Miruna Oprescu, Nathan Kallus
Accurately predicting conditional average treatment effects (CATEs) is crucial in personalized medicine and digital platform analytics. Since the treatments of interest often canno…
Peeking with PEAK: Sequential, Nonparametric Composite Hypothesis Tests for Means of Multiple Data Streams
Brian Cho, Kyra Gan, Nathan Kallus
We propose a novel nonparametric sequential test for composite hypotheses for means of multiple data streams. Our proposed method, \emph{peeking with expectation-based averaged cap…
Source Condition Double Robust Inference on Functionals of Inverse Problems
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
We consider estimation of parameters defined as linear functionals of solutions to linear inverse problems. Any such parameter admits a doubly robust representation that depends on…
Inference on Strongly Identified Functionals of Weakly Identified Functions
Andrew Bennett, Nathan Kallus, Xiaojie Mao +3
In a variety of applications, including nonparametric instrumental variable (NPIV) analysis, proximal causal inference under unmeasured confounding, and missing-not-at-random data…
What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment
Nathan Kallus
The fundamental problem of causal inference -- that we never observe counterfactuals -- prevents us from identifying how many might be negatively affected by a proposed interventio…
Long-term Causal Inference Under Persistent Confounding via Data Combination
Guido Imbens, Nathan Kallus, Xiaojie Mao +1
We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only…
Treatment Effect Risk: Bounds and Inference
Nathan Kallus
Since the average treatment effect (ATE) measures the change in social welfare, even if positive, there is a risk of negative effect on, say, some 10% of the population. Assessing…
Controlling for Unmeasured Confounding in Panel Data Using Minimal Bridge Functions: From Two-Way Fixed Effects to Factor Models
Guido Imbens, Nathan Kallus, Xiaojie Mao
We develop a new approach for identifying and estimating average causal effects in panel data under a linear factor model with unmeasured confounders. Compared to other methods tac…
Balanced Policy Evaluation and Learning for Right Censored Data
Owen E. Leete, Nathan Kallus, Michael G. Hudgens +2
Individualized treatment rules can lead to better health outcomes when patients have heterogeneous responses to treatment. Very few individualized treatment rule estimation methods…