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Guanglei Hong; Fan Yang; Xu Qin – Grantee Submission, 2023
In causal mediation studies that decompose an average treatment effect into indirect and direct effects, examples of post-treatment confounding are abundant. In the presence of treatment-by-mediator interactions, past research has generally considered it infeasible to adjust for a post-treatment confounder of the mediator-outcome relationship due…
Descriptors: Causal Models, Mediation Theory, Research Problems, Statistical Inference
Feller, Avi; Stuart, Elizabeth A. – Grantee Submission, 2021
Panel data methods, which include difference-in-differences and comparative interrupted time series, have become increasingly com- mon in education policy research. The key idea is to use variation across time and space (e.g., school districts) to estimate the effects of policy or programmatic changes that happen in some localities but not others.…
Descriptors: COVID-19, Pandemics, Educational Policy, Statistical Analysis
Peng Ding; Luke W. Miratrix – Grantee Submission, 2019
For binary experimental data, we discuss randomization-based inferential procedures that do not need to invoke any modeling assumptions. We also introduce methods for likelihood and Bayesian inference based solely on the physical randomization without any hypothetical super population assumptions about the potential outcomes. These estimators have…
Descriptors: Causal Models, Statistical Inference, Randomized Controlled Trials, Bayesian Statistics
Timothy Lycurgus; Ben B. Hansen; Mark White – Grantee Submission, 2022
We present an aggregation scheme that increases power in randomized controlled trials and quasi-experiments when the intervention possesses a robust and well-articulated theory of change. Intervention studies using longitudinal data often include multiple observations on individuals, some of which may be more likely to manifest a treatment effect…
Descriptors: Statistical Analysis, Randomized Controlled Trials, Quasiexperimental Design, Intervention
Doroudi, Shayan; Brunskill, Emma – Grantee Submission, 2017
In this paper, we investigate two purported problems with Bayesian Knowledge Tracing (BKT), a popular statistical model of student learning: "identifiability" and "semantic model degeneracy." In 2007, Beck and Chang stated that BKT is susceptible to an "identifiability problem"--various models with different…
Descriptors: Bayesian Statistics, Research Problems, Statistical Analysis, Models
Peng Ding; Fan Li – Grantee Submission, 2018
Inferring causal effects of treatments is a central goal in many disciplines. The potential outcomes framework is a main statistical approach to causal inference, in which a causal effect is defined as a comparison of the potential outcomes of the same units under different treatment conditions. Because for each unit at most one of the potential…
Descriptors: Attribution Theory, Causal Models, Statistical Inference, Research Problems

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