Dear Applied Statistics Workshop Community,
Our next meeting will be on Wednesday, March 6 (12:00 EST). Amanda
Coston presents "Addressing confounding in decision-making algorithms."
<When>
March 6, 12:00 to 1:30 PM, EST
Lunch will be available for pick-up inside CGIS K354.
<Where>
In-person: CGIS K354
Zoom:
https://harvard.zoom.us/j/93217566507?pwd=elBwYjRJcWhlVE5teE1VNDZoUXdjQT09
<Abstract>
Machine learning algorithms are used for decision-making in societally
high-stakes settings from child welfare and criminal justice to healthcare
and consumer lending. These algorithms are often intended to predict
outcomes under a proposed decision. It is challenging to evaluate how well
these algorithms perform because we only observe the relevant outcome under
a biased sample of the population. In this talk, we explore how to use
techniques from causal inference to estimate performance on the full
population. We will consider several strategies to account for confounding
factors that affect the decision and the outcome. First, we study runtime
confounding where all relevant factors are captured in the historical data,
but it is either undesirable or impermissible to use some such factors in
the prediction model. Second, we study the setting with unobserved
confounders where we can bound the degree to which the outcome varies on
average between units receiving different decisions conditional on observed
covariates and identified nuisance parameters. We develop debiased machine
learning estimators for the learning target and predictive performance
estimands under both settings. We present empirical results in the consumer
lending and child welfare domains.
Papers: arxiv:2212.09844 <https://arxiv.org/abs/2212.09844> and
arxiv:2006.16916 <https://arxiv.org/abs/2006.16916>.
<2023-2024 Schedule>
GOV 3009 Website:
https://projects.iq.harvard.edu/applied.stats.workshop-gov3009
Calendar:
https://calendar.google.com/calendar/u/0?cid=Y18zdjkzcGF2OWZqa2tsZHJidTlzbm…
Best,
Jialu
--
Jialu Li
Department of Government
Harvard University
*https://jialul.github.io/ <https://jialul.github.io/>*