In an earlier article, I made the case for causality, set a bit of theoretical ground, and argued that a solid understanding of causal mechanisms should be in the toolset of every data scientist informing key decisions through data. A causal inference analysis often entails drawing a graph of what may be causing what, identifying confounders, and stratifying those to find the effect of a treatment on an outcome. Doing this properly allows you to stay clear of spurious correlations and absurd claims. Finding the right confounders, which are the factors that influence both a treatment and an outcome is therefore key in solving for the causal effects. DoWhy is one of the most powerful libraries for the task, completely open-source by the Microsoft research team.

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.

In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step. The library is available at https://github.com/microsoft/dowhy

The human mind has a remarkable ability to associate causes with a specific event. From the outcome of an election to an object dropping on the floor, we are constantly associating chains of events that cause a specific effect. Neuropsychology refers to this cognitive ability as causal reasoning. Computer science and economics study a specific form of causal reasoning known as causal inference which focuses on exploring relationships between two observed variables. Over the years, machine learning has produced many methods for causal inference but they remain mostly difficult to use in mainstream applications.

For decades, causal inference methods have found wide applicability in the social and biomedical sciences. As computing systems start intervening in our work and daily lives, questions of cause-and-effect are gaining importance in computer science as well. To enable widespread use of causal inference, we are pleased to announce a new software library, DoWhy. Its name is inspired by Judea Pearl's do-calculus for causal inference. In addition to providing a programmatic interface for popular causal inference methods, DoWhy is designed to highlight the critical but often neglected assumptions underlying causal inference analyses.