Introduction to causality
=========================
DoWhy is based on a simple unifying language for causal inference, unifying two
powerful frameworks: 
causal graphs  and potential outcomes. It uses graph-based criteria and
do-calculus for modeling assumptions and identifying a non-parametric causal effect. 
For estimation, it switches to methods based primarily on potential outcomes.


For a quick introduction to causal inference, check out `amit-sharma/causal-inference-tutorial <https://github.com/amit-sharma/causal-inference-tutorial/>`_. We also gave a more comprehensive tutorial at the ACM Knowledge Discovery and Data Mining (`KDD 2018 <http://www.kdd.org/kdd2018/>`_) conference: `causalinference.gitlab.io/kdd-tutorial <http://causalinference.gitlab.io/kdd-tutorial/>`_. For an introduction to the four steps of causal inference and its implications for machine learning, you can access this video tutorial from Microsoft Research: `DoWhy Webinar <https://note.microsoft.com/MSR-Webinar-DoWhy-Library-Registration-On-Demand.html>`_.