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Title: Causal inference in spatio-temporal settings Authors:  Georgia Papadogeorgou - University of Florida (United States) [presenting]
Kosuke Imai - Harvard University (United States)
Jason Lyall - Dartmouth University (United States)
Fan Li - Duke University (United States)
Abstract: Many causal processes have spatial and temporal dimensions. Yet the classic causal inference framework is not directly applicable when the treatment and outcome variables are generated by spatio-temporal processes with an infinite number of possible event locations at each point in time. We take up the challenge of extending the potential outcomes framework and mediation analysis to these settings by formulating the treatment point process as a stochastic intervention. We develop an estimation technique that applies the inverse probability of treatment weighting method to spatially-smoothed outcome surfaces. We demonstrate that the proposed estimator is consistent and asymptotically normal as the number of time period approaches infinity. A primary advantage of our methodology is its ability to avoid structural assumptions about spatial spillover and temporal carryover effects. We use the proposed methods to estimate the effects of American airstrikes on insurgent violence in Iraq.