RMME/STAT Joint Colloquium

Experimental Evaluation of Algorithm-Assisted Human Decision-Making: Application to Pretrial Public Safety Assessment

Dr. Kosuke Imai
Harvard University

Friday, September 9, at 11:00AM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m486f7b13e6881ba895b350f338b0c90d

Despite an increasing reliance on fully-automated algorithmic decision-making in our day-to-day lives, human beings still make highly consequential decisions. As frequently seen in business, healthcare, and public policy, recommendations produced by algorithms are provided to human decision-makers to guide their decisions. While there exists a fast-growing literature evaluating the bias and fairness of such algorithmic recommendations, an overlooked question is whether they help humans make better decisions. We develop a general statistical methodology for experimentally evaluating the causal impacts of algorithmic recommendations on human decisions. We also show how to examine whether algorithmic recommendations improve the fairness of human decisions and derive the optimal decision rules under various settings. We apply the proposed methodology to preliminary data from the first-ever randomized controlled trial that evaluates the pretrial Public Safety Assessment (PSA) in the criminal justice system. A goal of the PSA is to help judges decide which arrested individuals should be released. On the basis of the preliminary data available, we find that providing the PSA to the judge has little overall impact on the judge’s decisions and subsequent arrestee behavior. Our analysis, however, yields some potentially suggestive evidence that the PSA may help avoid unnecessarily harsh decisions for female arrestees regardless of their risk levels while it encourages the judge to make stricter decisions for male arrestees who are deemed to be risky. In terms of fairness, the PSA appears to increase an existing gender difference while having little effect on any racial differences in judges’ decisions. Finally, we find that the PSA’s recommendations might be unnecessarily severe unless the cost of a new crime is sufficiently high.

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On June 2, 2022, Dr. Leslie Fierro (RMME Instructor and Co-Editor of New Directions for Evaluation) contributed to a panel session entitled, “Issues in Evaluation: Surveying the Evaluation Policy Landscape in 2022”. The Government Accountability Office (GAO) and Data Foundation co-sponsored this webinar in which panelists discussed the state of evaluation policy today. Visit this website and register to watch the recording of this excellent webinar for free! Congratulations on this work, Dr. Fierro!

RMME/STAT Joint Colloquium

Approximate Balancing Weights for Clustered Observational Study Designs

Dr. Luke Keele
University of Pennsylvania

Friday, April 29, at 3:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m35b82d4dc6d3e77536aa48390a02485b

In a clustered observational study, a treatment is assigned to groups and all units within the group are exposed to the treatment. Clustered observational studies are common in education where treatments are given to all students within some schools but withheld from all students in other schools. Clustered observational studies require specialized methods to adjust for observed confounders. Extant work has developed specialized matching methods that take key elements of clustered treatment assignment into account. Here, we develop a new method for statistical adjustment in clustered observational studies using approximate balancing weights. An approach based on approximate balancing weights improves on extant matching methods in several ways. First, our methods highlight the possible need to account for differential selection into clusters. Second, we can automatically balance interactions between unit level and cluster level covariates. Third, we can also balance high moments on key cluster level covariates. We also outline an overlap weights approach for cases where common support across treated and control clusters is poor. We introduce an augmented estimator that accounts for outcome information. We show that our approach has dual representation as an inverse propensity score weighting estimator based on a hierarchical propensity score model. We apply this algorithm to assess a school-based intervention through which students in treated schools were exposed to a new reading program during summer school. Overall, we find that balancing weights tend to produce superior balance relative to extant matching methods. Moreover, an approximate balancing weight approach tends to require less input from the user to achieve high levels of balance.

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RMME Evaluation Colloquium

UConn’s Institutional Review Board: A Closer Look at Ethics in Research & Evaluation

Cassandra Myers, The HRP Consulting Group
Joan Levine, University of Connecticut

Friday, April 1, at 3:00PM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=me8fe20df2d511c754f1bd3f3539991b4

The UConn-Storrs Human Research Protection Program (HRPP) is dedicated to the protection of human subjects in research activities conducted under its auspices. The HRPP reviews human subjects research to ensure appropriate safeguards for the ethical, compliant, and safe conduct of research, as well as the protection of the rights and welfare of the human subjects who volunteer to participate. As the regulatory framework for the protections of human subjects is complex and multi-faceted, this session’s goals are to review the regulatory framework and how it applies to research and evaluation, the requirements for consent, when consent can be waived, and how to navigate the IRB process at UConn. This session will also review historical case studies to understand current requirements and how these events still affect populations, policies, and regulations.

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