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      Bayesian method for causal inference in spatially-correlated multivariate time series

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          Abstract

          Measuring the causal impact of an advertising campaign on sales is an important problem for advertising companies interested in modeling consumer demand at stores in different locations. This paper proposes a new causal inference method that uses a Bayesian multivariate time series model to capture the spatial correlation between stores. Control stores which are used to build counterfactuals over the causal period are chosen before running the advertising campaign. The novelty of this method is to estimate causal effects by comparing the posterior distributions of latent variables given by the observed data and its counterfactual data. We use one-sided Kolmogorov-Smirnov distance to quantify the difference between the two posterior distributions. We found that this method is able to detect smaller scale of causal impact as measurement errors are automatically filtered out in the causal analysis compared to a commonly used method. A two-stage algorithm is used to estimate the model. A G-Wishart prior with a given graphical structure on the precision matrix is used to impose sparsity in spatial correlation. The graphical structure needs not correspond to a decomposable graph. We model the local linear trend by a stationary multivariate autoregressive process to prevent the prediction intervals from being explosive. A detailed simulation study shows the effectiveness of the proposed approach to causal inference. We apply the proposed method to a real dataset to measure the effect of an advertising campaign for a consumer product sold at stores of a large national retail chain.

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          Matching Methods for Causal Inference: A Review and a Look Forward

            (2010)
          When estimating causal effects using observational data, it is desirable to replicate a randomized experiment as closely as possible by obtaining treated and control groups with similar covariate distributions. This goal can often be achieved by choosing well-matched samples of the original treated and control groups, thereby reducing bias due to the covariates. Since the 1970s, work on matching methods has examined how to best choose treated and control subjects for comparison. Matching methods are gaining popularity in fields such as economics, epidemiology, medicine and political science. However, until now the literature and related advice has been scattered across disciplines. Researchers who are interested in using matching methods---or developing methods related to matching---do not have a single place to turn to learn about past and current research. This paper provides a structure for thinking about matching methods and guidance on their use, coalescing the existing research (both old and new) and providing a summary of where the literature on matching methods is now and where it should be headed.
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            Semiparametric Difference-in-Differences Estimators

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              Inference with Difference-in-Differences and Other Panel Data

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                Author and article information

                Journal
                18 January 2018
                Article
                1801.06282

                http://creativecommons.org/licenses/by-nc-sa/4.0/

                Custom metadata
                31 pages, 7 figures
                stat.ME

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