19 citations · 58 across the 15 of their papers we have counts for
15 papers
Disentangling Causal Mechanisms in Conjoint Experiments Using Mediation
Michaël Aklin, Max Goplerud, Nicole E. Pashley +1
Conjoint experiments provide an attractive way to assess the role of multiple attributes simultaneously on decision-making. However, the randomization of multiple attributes preven…
Stratified Sampling for Model-Assisted Estimation with Surrogate Outcomes
Reagan Mozer, Nicole E. Pashley, Luke Miratrix
In many randomized trials, outcomes such as essays or open-ended responses must be manually scored as a preliminary step to impact analysis, a process that is costly and limiting.…
Analysis and sample-size determination for audit experiments with binary response and application to identification of effect of racial discrimination on access to justice
Nicole Pashley, Brian Libgober, Tirthankar Dasgupta
Social scientists have increasingly turned to audit experiments to investigate discrimination in the market for jobs, loans, housing and other opportunities. In a typical audit exp…
Bounds on causal effects in factorial experiments with non-compliance
Matthew Blackwell, Nicole E. Pashley
Factorial experiments are ubiquitous in the social and biomedical sciences, but when units fail to comply with each assigned factors, identification and estimation of the average t…
Design-based Causal Inference for Incomplete Block Designs
Taehyeon Koo, Nicole E. Pashley
Researchers often turn to block randomization to increase the precision of their inference or due to practical considerations, such as in multisite trials. However, if the number o…
Optimal allocation of sample size for randomization-based inference from factorial designs
Arun Ravichandran, Nicole E. Pashley, Brian Libgober +1
Optimizing the allocation of units into treatment groups can help researchers improve the precision of causal estimators and decrease costs when running factorial experiments. Howe…
Improving instrumental variable estimators with post-stratification
Nicole E. Pashley, Luke Keele, Luke W. Miratrix
Experiments studying get-out-the-vote (GOTV) efforts estimate the causal effect of various mobilization efforts on voter turnout. However, there is often substantial noncompliance…
Causal inference from treatment-control studies having an additional factor with unknown assignment mechanism
Nicole E. Pashley, Kristen B. Hunter, Katy McKeough +2
Consider a situation with two treatments, the first of which is randomized but the second is not, and the multifactor version of this. Interest is in treatment effects, defined usi…
Estimating Heterogeneous Causal Effects of High-Dimensional Treatments: Application to Conjoint Analysis
Max Goplerud, Kosuke Imai, Nicole E. Pashley
Estimation of heterogeneous treatment effects is an active area of research. Most of the existing methods, however, focus on estimating the conditional average treatment effects of…
Block what you can, except when you shouldn't
Nicole E. Pashley, Luke W. Miratrix
Several branches of the potential outcome causal inference literature have discussed the merits of blocking versus complete randomization. Some have concluded it can never hurt the…
Conditional As-If Analyses in Randomized Experiments
Nicole E. Pashley, Guillaume W. Basse, Luke W. Miratrix
The injunction to `analyze the way you randomize' is well-known to statisticians since Fisher advocated for randomization as the basis of inference. Yet even those convinced by the…
Design-Based Ratio Estimators and Central Limit Theorems for Clustered, Blocked RCTs
Peter Z. Schochet, Nicole E. Pashley, Luke W. Miratrix +1
This article develops design-based ratio estimators for clustered, blocked randomized controlled trials (RCTs), with an application to a federally funded, school-based RCT testing…
Note on the Delta Method for Finite Population Inference with Applications to Causal Inference
Nicole E. Pashley
The delta method creates more general inference results when coupled with central limit theorem results for the finite population. This opens up a range of new estimators for which…
Causal Inference for Multiple Treatments using Fractional Factorial Designs
Nicole E. Pashley, Marie-Abele C. Bind
We consider the design and analysis of multi-factor experiments using fractional factorial and incomplete designs within the potential outcome framework. These designs are particul…
Insights on Variance Estimation for Blocked and Matched Pairs Designs
Nicole E. Pashley, Luke W. Miratrix
Evaluating blocked randomized experiments from a potential outcomes perspective has two primary branches of work. The first focuses on larger blocks, with multiple treatment and co…