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Title: Variable selection and estimation for the average treatment effect with error-prone confounders Authors:  Grace Yi - University of Western Ontario (Canada)
Li-Pang Chen - National Chengchi University (Taiwan) [presenting]
Abstract: In the framework of causal inference, the inverse-probability-weighting estimation method and its variants have been commonly employed to estimate the average treatment effect. Such methods, however, are challenged by the presence of irrelevant pre-treatment variables and measurement errors. Ignoring these features and naively applying the usual inverse probability-weighting estimation procedures may typically yield biased inference results. We develop an inference method for estimating the average treatment effect with those features taken into account. We establish theoretical properties for the resulting estimator and carry out numerical studies to assess the finite sample performance of the proposed estimator.