Mark your calendar! The Modern Modeling Methods (M³) conference returns to UConn after a lengthy pandemic-induced hiatus. From June 26-28, 2023, M³ will resume as an in-person conference on the Storrs campus. Keynote speakers and workshop presenters include Bengt Muthen, Tihomir Asparouhov, and Ellen Hamaker. Remember to check the M³ website regularly for more information and updates.

RMME/STAT Joint Colloquium

Testing an Elaborate Theory of a Causal Hypothesis

Dr. Dylan Small
University of Pennsylvania

Friday, November 11, at 11AM ET

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m8da0e35b64c861fc97a21dd36fb29ded

When R.A. Fisher was asked what can be done in observational studies to clarify the step from association to causation, he replied, “Make your theories elaborate” — when constructing a causal hypothesis, envisage as many different consequences of its truth as possible and plan observational studies to discover whether each of these consequences is found to hold. William Cochran called “this multi-phasic attack…one of the most potent weapons in observational studies.” Statistical tests for the various pieces of the elaborate theory help to clarify how much the causal hypothesis is corroborated. In practice, the degree of corroboration of the causal hypothesis has been assessed by verbally describing which of the several tests provides evidence for which of the several predictions. This verbal approach can miss quantitative patterns. So, we developed a quantitative approach to making statistical inference about the amount of the elaborate theory that is supported by evidence. This is joint work with Bikram Karmakar.

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RMME/STAT Joint Colloquium

Searching for Truth through Data

Dr. Edsel A. Pena
University of South Carolina

Friday, October 7, at 11:15AM ET, AUST 108

https://uconn-cmr.webex.com/uconn-cmr/j.php?MTID=m9667e91caf1197b47fc45f50529388b9

This talk concerns the role of statistical thinking in the Search for Truth using data. This will bring us to a discussion of p-values, a much-used tool in scientific research, but at the same time a controversial concept which has elicited much, sometimes heated, debate and discussion. In March 2016, the American Statistical Association (ASA) was compelled to release an official statement regarding p-values; a psychology journal has even gone to the extreme of banning the use of p-values in its articles; and in 2018, a special issue of The American Statistician was fully devoted to this issue. A main concern in the use of p-values is the introduction of a somewhat artificial threshold, usually the value of 0.05, when used in decision-making, with implications on reproducibility and replicability of reported scientific results. Some new perspectives on the use of p-values and in the search for truth through data will be discussed. In particular, this will touch on the representation of knowledge and its updating based on observations. Related to the issue of p-values, the following question arises: “When given the p-value, what does it provide in the context of the updated knowledge of the phenomenon under consideration, and what additional information should accompany it?” To be addressed also is the question of whether it is time to move away from hard thresholds such as 0.05 and whether we are on the verge of — to quote Wasserstein, Schirm and Lazar (2019) — a “World Beyond P < 0.05.”

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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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