Dean Knox (Workshop in Applied Statistics)

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Today's presenter:

Dean Knox (University of Pennsylvania), "ε-sharp Bounds for Partially Observed Causal Processes: Testing for Racial Bias in Policing by Fusing Incomplete Records"

Conference link:

https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09

Abstract:

Social scientists often possess fragmented information about subsets and aspects of the complex causal processes they study. Research on police-civilian interactions, for example, is complicated not only by undocumented interactions, but inconsistent recording of events within documented interactions. These data constraints can lead to a proliferation of incompatible analytic approaches relying on contradictory unstated assumptions, impeding scientific progress on important questions like the severity of racial bias in policing. Nonparametric sharp bounds, or the tightest possible range of answers consistent with available data, offer a path forward: claims outside the bounds can be immediately rejected, and claims inside the bounds must explicitly justify the additional assumptions that enable tightening. However, we show proving sharpness is NP-hard for broad classes of data constraints and causal quantities, rendering this approach computationally infeasible for even moderately sized causal processes. We present an efficient spatial branch-and-bound procedure with a theoretical guarantee that we term "ε-sharpness," indicating the worst-case looseness factor of the relaxed bounds relative to the (unknown) completely sharp bounds. The procedure is guaranteed to attain complete sharpness with sufficient computation time. We present results on asymptotic validity of and conservative statistical inference for ε-sharp bounds. The technique is illustrated using simulations using common research designs in the study of policing.

The Applied Statistics Workshop (Gov 3009) is a forum for graduate students, faculty, and visiting scholars to present and discuss methodological or empirical work in progress in an interdisciplinary setting. The workshop features a tour of Harvard's statistical innovations and applications with weekly stops in different fields and disciplines and includes occasional presentations by invited speakers. It is co-hosted by Harvard University's Department of Government and Institute for Quantitative Social Science (IQSS).

For more information, visit the Applied Statistics website.