Thread by @DeepMind

We’re back with more #AtHomeWithAI researcher recommendations. Next up is research scientist @csilviavr with suggestions for resources to learn about causal inference! (1/5)

Her first suggestion is “The Book of Why” by @yudapearl & Dana Mackenzie.

According to Silvia, this is best for those looking for an introduction to the topic: bit.ly/30isGej #AtHomeWithAI

Need a more in-depth look at causal inference? Silvia suggests reading through “Causal Inference in Statistics: A Primer” by @yudapearl, @MadelynTheRose & @NP_Jewell.

bit.ly/36xdvza #AtHomeWithAI

The Elements of Causal Inference is highly accessible for machine learning researchers, according to Silvia. It offers a self-contained & concise introduction to causal models and how to learn them from data.

bit.ly/3gCqowy #AtHomeWithAI

Interested in different ways to formulate causal inference operations in a Bayesian network?

Silvia’s final suggestion is to read through @PhilDawid’s recently published overview on the topic: bit.ly/2XFjAGR #AtHomeWithAI