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DESCRIPTION:Title: Counterfactual Imputation via Matrix Completion with Sta
 ggered Treatment Implementation\n\nAbstract: An important problem in the s
 ocial sciences is estimating the causal effect of a binary treatment on a 
 continuous outcome over time. A recently proposed matrix completion method
  for counterfactual imputation decomposes observed outcomes into matrices 
 of latent factors and factor loadings and imputes missing potential outcom
 es based on the estimated factors and loadings. The estimator uses matrix 
 norm regularization to produce a low-dimensional representation of the obs
 erved outcomes and thereby improve generalizability when imputing the miss
 ing (counterfactual) values. I focus on a novel “retrospective” framework 
 that uses units exposed to treatment throughout the panel (always-treated)
  to form a control group when never-treated units are unavailable. The tar
 get population consists of switch-treated units that enter treatment after
  an initial time\, which varies across units. Two extensions to the estima
 tor are proposed: (i.) weighting the loss function by the propensity score
  to correct for imbalances in the covariate distributions between the obse
 rved and missing values\; and (ii.) imputing endogenous covariate values w
 hen estimating potential outcomes. An evaluation of the effect of European
  integration on cross-border employment illustrates the method and framewo
 rk. This talk is based on joint work with Andrea Albanese (LISER)\, Andrea
  Mercatanti (University of Verona)\, and Fan Li (Duke).\n\n \n\n \n\nhttps
 ://uqam.zoom.us/j/87540246412\n
DTSTART:20220217T203000Z
DTEND:20220217T213000Z
SUMMARY:Jason Poulos\, Harvard Medical School
URL:https://www.mcgill.ca/mathstat/channels/event/jason-poulos-harvard-medi
 cal-school-337585
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