Wrapping it up
In this chapter, we discussed the Python causal ecosystem. We introduced the DoWhy and EconML libraries and practiced the four-step causal inference process using DoWhy’s CausalModel API. We learned how to automatically obtain estimands and how to use different types of estimators to compute causal effect estimates. We discussed what refutation tests are and how to use them in practice. Finally, we introduced DoWhy’s experimental GCM API and showed its great capabilities when it comes to answering various causal queries. After working through this chapter, you have the basic skills to apply causal inference to your own problems. Congratulations!
In the next chapter, we’ll summarize common assumptions for causal inference and discuss some limitations of the causal inference framework.
