gov3009-l February 2021

gov3009-l@lists.fas.harvard.edu

1 participants
4 discussions

Gov 3009 Applied Statistics Workshop (2/24) -- James Robins

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, February 24, where James Robins presents research on "Estimation of Optimal Testing and Treatment Regimes under No Direct Effect (NDE) of Testing." *Abstract*: In this talk I describe new, highly efficient estimators of optimal joint testing and treatment regimes under the no direct effect assumption that a given laboratory, diagnostic, or screening test has no effect on a patient's clinical outcomes, except through the effect of the test results on the choice of treatment. The proposed estimators attain high efficiency because they leverage this "no direct effect of testing" (abbreviated as NDE) assumption. What is surprising and, indeed, unprecedented in my experience, is that, in a substantive study of HIV infected subjects, our new estimators delivered a 50-fold increase in efficiency (and, thus, a 50 fold reduction in required sample size) compared to estimators that fail to leverage the NDE assumption! In this talk I review the results of this HIV study, describe the new estimators, and provide guidance as to when such large gains in efficiency are to be expected. Areas in which our new, more efficient estimators should be particularly important is that of cost-benefit analyses wherein the costs of diagnostic tests (such as MRIs to screen for lung cancer, mammograms to screen for breast cancer, and urinary cytology to screen for bladder cancer) are weighed against the clinical value of the information supplied by the test results. In a political science context, candidates often conduct private polls and focus groups to help update campaign outreach decisions such as the number and content of social media and television ad buys to be allocated to various demographic groups. One can view private polls and focus groups as tests and outreach decisions as treatments that together satisfy no direct effect of testing on the outcome of the election except through the test results on the choice of treatment. *Message to the audience*: The speaker Jamie Robins suggests you read the following less technical sections of the paper <https://projects.iq.harvard.edu/files/applied.stats.workshop-gov3009/files/…> that cover many of the main ideas. Section 1: Introduction Section 4: The NDE of Testing Assumption Section 5.1 Section 7 Section 8 and 8.1 stopping after remark 7 *Link to the paper*: URL <https://projects.iq.harvard.edu/files/applied.stats.workshop-gov3009/files/…> *Zoom link*: https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 2 months

Gov 3009 Applied Statistics Workshop (2/17) -- Soichiro Yamauchi

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, February 17 (tomorrow), where I (Soichiro Yamauchi) will present "Adjusting for Unmeasured Confounding in Marginal Structural Models with Propensity-Score Fixed Effects." This is joint work with Matthew Blackwell (Harvard University). *Abstract*: Marginal structural models are a popular tool for investigating the effects of time-varying treatments, but they require the assumption that there are no unobserved confounders between the treatment and outcome. With observational data, this assumption may be difficult to maintain, and in studies with panel data, many researchers use fixed effects models to purge the data of time-constant unmeasured confounding. Unfortunately, traditional linear fixed effects models are not suitable for marginal structural models, since they can only estimate lagged effects under implausible assumptions. To resolve this tension, we propose a novel inverse probability of treatment weighting estimator with propensity-score fixed effects to adjust for time-constant unmeasured confounding in marginal structural models. We show that, in spite of the incidental parameters problem, these estimators are consistent and asymptotically normal when the number of units and time periods grow at a similar rate. Unlike traditional fixed effect models, this approach works even when the outcome is only measured at a single point in time as is common in marginal structural models. We apply these methods to estimate the effect of negative advertising on the electoral success of candidates for statewide offices in the United States. *Zoom link: * https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop:* https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all tomorrow! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 2 months

Gov 3009 Applied Statistics Workshop (2/10) -- Dean Knox

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, February 10, where we will hear Dean Knox <http://www.dcknox.com/> (University of Pennsylvania) presents research on "ε-sharp Bounds for Partially Observed Causal Processes: Testing for Racial Bias in Policing by Fusing Incomplete Records." *Authors:* Guilherme Duarte, Dean Knox, Jonathan Mummolo *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. *Zoom link: * https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop:* https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all on Wednesday! Best, Soichiro

3 years, 2 months

Gov 3009 Applied Statistics Workshop (2/3) -- Alex Tarr

by Yamauchi, Soichiro

Hi all, Our next virtual meeting will be at 12pm (EST) Wednesday, February 3 (tomorrow), where we will hear Alex Tarr (Princeton University) presents research on "Estimating Average Treatment Effects with Support Vector Machines." *Abstract*: Support vector machine (SVM) is one of the most popular classification algorithms in the machine learning literature. We demonstrate that SVM can be used to balance covariates and estimate average causal effects under the unconfoundedness assumption. We show that the SVM cost parameter controls the trade-off between covariate balance and subset size, and as a result, existing SVM regularization path algorithms can be used to compute the balance-sample size frontier. We then characterize the bias of causal effect estimation arising from this tradeoff, connecting the proposed SVM procedure to the existing kernel balancing methods. Finally, we conduct simulation and empirical studies to evaluate the performance of the proposed methodology and find that SVM is competitive with the state-of-the-art covariate balancing methods. *Zoom link*: https://harvard.zoom.us/j/97787602526?pwd=Uzh3bVVVS0F4TEVYQTJlV3BQNjcydz09 *Schedule of the workshop*: https://projects.iq.harvard.edu/applied.stats.workshop-gov3009 Looking forward to seeing you all tomorrow! Best, Soichiro -- Soichiro Yamauchi PhD candidate Harvard University URL: https://soichiroy.github.io/

3 years, 2 months

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gov3009-l February 2021