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TFDIFF: Stata module to compute pre- and post-treatment estimation of the Average Treatment Effect (ATE) with fixed binary treatment


  • Giovanni Cerulli

    () (IRCrES-CNR)

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tfdiff estimates Average Treatment Effects (ATEs) when the treatment is binary and fixed to a specific point in time. It assumes the availability of a panel dataset where the same treated and untreated units are observed over time. Using tfdiff, the user can estimate the pre- and post-intervention effects by selecting the intervention time t. The results are plotted in an easy-to-read graphical representation. In order to assess the reliability of the causal results achieved by the user's specified model, tfdiff allows to test the "parallel trend" assumption via a joint significance test on the pre-treatment effects. The model estimated by tfdiff is a generalization of the Difference-In-Differences (DID) approach to more than one pre- and post-treatment periods and fixed treatment time.

Suggested Citation

  • Giovanni Cerulli, 2019. "TFDIFF: Stata module to compute pre- and post-treatment estimation of the Average Treatment Effect (ATE) with fixed binary treatment," Statistical Software Components S458696, Boston College Department of Economics, revised 16 Dec 2020.
  • Handle: RePEc:boc:bocode:s458696
    Note: This module should be installed from within Stata by typing "ssc install tfdiff". The module is made available under terms of the GPL v3 ( Windows users should not attempt to download these files with a web browser.

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