27 citations · 116 across the 29 of their papers we have counts for
24 papers · 1 filter
Improving the Estimation of Site-Specific Effects and their Distribution in Multisite Trials
JoonHo Lee, Jonathan Che, Sophia Rabe-Hesketh +2
In multisite trials, researchers are often interested in several inferential goals: estimating treatment effects for each site, ranking these effects, and studying their distributi…
Augmented balancing weights as linear regression
David Bruns-Smith, Oliver Dukes, Avi Feller +1
We provide a novel characterization of augmented balancing weights, also known as automatic debiased machine learning (AutoDML). These popular doubly robust or de-biased machine le…
Outcome Assumptions and Duality Theory for Balancing Weights
David Bruns-Smith, Avi Feller
We study balancing weight estimators, which reweight outcomes from a source population to estimate missing outcomes in a target population. These estimators minimize the worst-case…
The Balancing Act in Causal Inference
Eli Ben-Michael, Avi Feller, David A. Hirshberg +1
The idea of covariate balance is at the core of causal inference. Inverse propensity weights play a central role because they are the unique set of weights that balance the covaria…
Estimating the effects of a California gun control program with Multitask Gaussian Processes
Eli Ben-Michael, David Arbour, Avi Feller +2
Gun violence is a critical public safety concern in the United States. In 2006 California implemented a unique firearm monitoring program, the Armed and Prohibited Persons System (…
Is it who you are or where you are? Accounting for compositional differences in cross-site treatment variation
Benjamin Lu, Eli Ben-Michael, Avi Feller +1
Multisite trials, in which treatment is randomized separately in multiple sites, offer a unique opportunity to disentangle treatment effect variation due to "compositional" differe…
Interpretable Sensitivity Analysis for Balancing Weights
Dan Soriano, Eli Ben-Michael, Peter J. Bickel +2
Assessing sensitivity to unmeasured confounding is an important step in observational studies, which typically estimate effects under the assumption that all confounders are measur…
Multilevel calibration weighting for survey data
Eli Ben-Michael, Avi Feller, Erin Hartman
In the November 2016 U.S. presidential election, many state level public opinion polls, particularly in the Upper Midwest, incorrectly predicted the winning candidate. One leading…
A trial emulation approach for policy evaluations with group-level longitudinal data
Eli Ben-Michael, Avi Feller, Elizabeth A. Stuart
To limit the spread of the novel coronavirus, governments across the world implemented extraordinary physical distancing policies, such as stay-at-home orders, and numerous studies…
COVID-19 Policy Impact Evaluation: A guide to common design issues
Noah A Haber, Emma Clarke-Deelder, Joshua A Salomon +2
Policy responses to COVID-19, particularly those related to non-pharmaceutical interventions, are unprecedented in scale and scope. Epidemiologists are more involved in policy deci…
Varying impacts of letters of recommendation on college admissions: Approximate balancing weights for subgroup effects in observational studies
Eli Ben-Michael, Avi Feller, Jesse Rothstein
In a pilot program during the 2016-17 admissions cycle, the University of California, Berkeley invited many applicants for freshman admission to submit letters of recommendation. W…
Synthetic Controls with Staggered Adoption
Eli Ben-Michael, Avi Feller, Jesse Rothstein
Staggered adoption of policies by different units at different times creates promising opportunities for observational causal inference. Estimation remains challenging, however, an…
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…
Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge
Carlos Carvalho, Avi Feller, Jared Murray +2
A growing number of methods aim to assess the challenging question of treatment effect variation in observational studies. This special section of "Observational Studies" reports 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…
The Augmented Synthetic Control Method
Eli Ben-Michael, Avi Feller, Jesse Rothstein
The synthetic control method (SCM) is a popular approach for estimating the impact of a treatment on a single unit in panel data settings. The "synthetic control" is a weighted ave…
Flexible sensitivity analysis for observational studies without observable implications
Alexander Franks, Alexander D'Amour, Avi Feller
A fundamental challenge in observational causal inference is that assumptions about unconfoundedness are not testable from data. Assessing sensitivity to such assumptions is theref…
Algorithmic Decision Making in the Presence of Unmeasured Confounding
Jongbin Jung, Ravi Shroff, Avi Feller +1
On a variety of complex decision-making tasks, from doctors prescribing treatment to judges setting bail, machine learning algorithms have been shown to outperform expert human jud…
Identifying and Estimating Principal Causal Effects in Multi-site Trials
Lo-Hua Yuan, Avi Feller, Luke W. Miratrix
Randomized trials are often conducted with separate randomizations across multiple sites such as schools, voting districts, or hospitals. These sites can differ in important ways,…
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…
Bounding, an accessible method for estimating principal causal effects, examined and explained
Luke Miratrix, Jane Furey, Avi Feller +2
Estimating treatment effects for subgroups defined by post-treatment behavior (i.e., estimating causal effects in a principal stratification framework) can be technically challengi…
Principal Score Methods: Assumptions and Extensions
Avi Feller, Fabrizia Mealli, Luke Miratrix
Researchers addressing post-treatment complications in randomized trials often turn to principal stratification to define relevant assumptions and quantities of interest. One appro…
Weak separation in mixture models and implications for principal stratification
Avi Feller, Evan Greif, Nhat Ho +2
Principal stratification is a widely used framework for addressing post-randomization complications. After using principal stratification to define causal effects of interest, rese…
Randomization Inference for Treatment Effect Variation
Peng Ding, Avi Feller, Luke Miratrix
Applied researchers are increasingly interested in whether and how treatment effects vary in randomized evaluations, especially variation not explained by observed covariates. We p…