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Bipartite Causal Inference with Interference: Estimating Health Impacts of Power Plant Regulations
Add to Calendar 2019-11-14T20:30:00 2019-11-14T21:30:00 UTC Bipartite Causal Inference with Interference: Estimating Health Impacts of Power Plant Regulations Thomas Building (201)
Start DateThu, Nov 14, 2019
3:30 PM
to
End DateThu, Nov 14, 2019
4:30 PM
Presented By
Corwin Zigler, University of Texas at Austin
Event Series:

A fundamental feature of evaluating causal health effects of air quality regulations is that air pollution moves through space, rendering health outcomes at a particular population location dependent upon regulatory actions taken at multiple, possibly distant, pollution sources. Motivated by studies of the public-health impacts of power plant regulations in the U.S., this talk introduces the novel setting of bipartite causal inference with interference, which arises when 1) treatments are defined on observational units that are distinct from those at which outcomes are measured and 2) there is interference between units in the sense that outcomes for some units depend on the treatments assigned to many other units. Interference in this setting arises due to complex exposure patterns dictated by physical-chemical atmospheric processes of pollution transport, with intervention effects framed as propagating across a bipartite network of power plants and residential zip codes. New causal estimands are introduced for the bipartite setting, along with an estimation approach based on generalized propensity scores for treatments on a network. The new methods are deployed to estimate how emission-reduction technologies implemented at coal-fired power plants causally affect health outcomes among Medicare beneficiaries in the U.S..