Wrapping it up
We covered a lot in this chapter. We started by revisiting the S-Learner and T-Learner models and demonstrated how flexible deep learning architectures can help combine the benefits of both models. We implemented TARNet and SNet and learned how to use the PyTorch-based CATENets library.
Next, we delved into the application of causality in NLP. We used a Transformer-based CausalBert model to compute the average treatment effect of a gender avatar on the probability of getting an upvote in a simulated Reddit-like discussion forum.
Finally, we took a glimpse into the world of econometrics and quasi-experimental data and learned how to implement a Bayesian synthetic control estimator using CausalPy.
In the next chapter, we’ll start our adventure with causal discovery.
See you on the other side!
