  Estimation of causal effects involves crucial assumptions about the
data-generating process, such as directionality of effect, presence of
instrumental variables or mediators, and whether all relevant confounders are
observed. Violation of any of these assumptions leads to significant error in
the effect estimate. However, unlike cross-validation for predictive models,
there is no global validator method for a causal estimate. As a result,
expressing different causal assumptions formally and validating them (to the
extent possible) becomes critical for any analysis. We present DoWhy, a
framework that allows explicit declaration of assumptions through a causal
graph and provides multiple validation tests to check a subset of these
assumptions. Our experience with DoWhy highlights a number of open questions
for future research: developing new ways beyond causal graphs to express
assumptions, the role of causal discovery in learning relevant parts of the
graph, and developing validation tests that can better detect errors, both for
average and conditional treatment effects. DoWhy is available at
https://github.com/microsoft/dowhy.
