El Departamento de Economía de la Universidad de Chile tiene el agrado de invitar a usted a un nuevo Seminario Académico:

Título -  ¨Stable Weights that Balance Covariates for Causal Inference and Estimation with Incomplete Outcome Data¨

Presentador - José Zubizarreta

Abstract - Weighting methods that adjust for observed covariates, such as inverse probability weighting, are widely used for causal inference and estimation with incomplete outcome data. Part of the appeal of such methods is that one set of weights can be used to estimate a range of treatment effects based on different outcomes, or a variety of population means for several variables. However, this appeal can be diminished in practice by the instability of the estimated weights and by the difficulty of adequately adjusting for observed covariates in some settings. To address these limitations, this paper presents a new weighting method that finds the weights of minimum variance that adjust or balance the empirical distribution of the observed covariates up to levels prespecified by the researcher. This method allows the researcher to balance very precisely the means of the observed covariates and other features of their marginal and joint distributions, such as variances and correlations and also, for example, the quantiles of interactions of pairs and triples of observed covariates, thus balancing entire two- and three-way marginals. Since the weighting method is based on a well-defined convex optimization problem, duality theory provides insight into the behavior of the variance of the optimal weights in relation to the level of covariate balance adjustment, answering the question, how much does tightening a balance constraint increases the variance of the weights? Also, the weighting method runs in polynomial time so relatively large data sets can be handled quickly. An implementation of the method is provided in the new package sbw for R. This paper shows some theoretical properties of the resulting weights and illustrates their use by analyzing both a real data set and a simulated example.

Bio - José Zubizarreta es Profesor Asistente en la División de Decisiones, Riesgo y Operaciones, y en el Departamento de Estadísticas de la Universidad de Columbia en Nueva York.  Su trabajo de investigación se centra en desarrollar métodos estadísticos para hacer inferencia causal en experimentos aleatorizados y estudios observacionales, y en estimación con datos faltantes.  Entre otros, su trabajo ha sido publicado en el Journal of the American Statistical Association, The Annals of Applied Statistics y Biometrika.  Entre otros reconocimientos, en 2014 recibió el Kenneth Rothman Award por la mejor publicación en Epidemiology en 2013, y su investigación ha sido financiada por la Alfred P. Sloan Foundation.

El  Seminario  se realizará el Lunes 11 de enero de 13:00 a 14:00 hrs, en  la Sala P-307 Tercer Piso del edificio Placa de la Facultad de Economía y Negocios de la Universidad de Chile, ubicada en Diagonal Paraguay 257.