About MDRC
Htet is a data analyst at MDRC’s Center for Data Insights. Since joining MDRC, in the fall of 2018, Zarni has co-created a reusable predictive analytics assessment tool (PAAT); currently, he is working on its methodological and codebase expansion on machine learning model fairness and interpretability. He is also co-authoring an internal guide on predictive modeling in the context of social policy programs. One of Htet’s favorite projects involved collaborating with researchers from a state agency to apply predictive modeling to improve the agency’s program outcomes while being rigorous about data and model ethics. He also worked on developing MDRC’s first public R Shiny app for statistical power estimation.
Before joining MDRC, Htet was a Data Science for Public Good Fellow at the Social Decision Analytics Lab in Ballston, MD, where he worked on data deduplication, probabilistic record linkage, and synthetic data creation, among other topics. Htet has a graduate degree in applied statistics from New York University, where his course work and independent projects included causal inference and multilevel models.
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MDRC Publications
ReportA Toolkit for State and Local Agencies on How to Access, Link, and Analyze Unemployment Insurance Wage Data
November, 2022Temporary Assistance for Needy Families (TANF) agencies are increasingly focused on using administrative data to assess how well programs are working and to inform policies and best practices. This toolkit was created to help TANF professionals develop more robust practices using employment data for program monitoring, reporting, and evaluation.
MethodologyJune, 2022Multiple testing procedures reduce the likelihood of false positive findings, but can also reduce the probability of detecting true effects. This post introduces two open-source software tools from the Power Under Multiplicity Project that can help researchers plan analyses for randomized controlled trials using multiple testing procedures.
BriefEvidence from Child First
May, 2022This brief presents results from a proof-of-concept exercise that examined the potential benefits of using predictive analytics to improve service delivery by Child First, a program that provides therapeutic support to families with young children. The information may be useful for other organizations interested in implementing these cutting-edge tools.
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Other Publications
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Projects
Across the social sector, government agencies, educational institutions, and nonprofit organizations are all benefiting from greater access both to more detailed and frequent data and to a variety of options for increased computing power. With data-science tools and guidance in applying them, practitioners can harness multiple sources of data to gain new insights about the individuals they serve, the contexts in which they operate, their staff members, and their program features. When such tools are incorporated into daily operations in a responsible way, they can help practitioners improve their programs and the lives of those they serve.