DoWhy provides a principled, end-to-end approach to causal inference. [Blog post](link) | [Github](gf)
If you are interested in causal inference, check out one of my tutorials from [IC2S2](f) or [KDD](kdd-link). [Quick Intro](http://github.com/amit-sharma/causal-inference-tutorial) | [KDD 2018 Tutorial](http://causalinference.gitlab.io/kdd-tutorial/)
That we find out the cause of this effect,
Or rather say, the cause of this defect,
For this effect defective comes by cause.
-Hamlet by William Shakespeare
A few years back I stumbled upon the world of causality. Two things became immediately clear. First, it is a fascinating topic spanning some of the greatest scholars in philosophy, logic and statistics. Second, and most unfortunately, the literature on it tends to be a dense, uninviting tome.
Two roads diverged in a wood, and I—
I took the one less traveled by,
But if I could go back, I will try
To take both: why one, then another.
When I first heard about doubly robust estimation, it sounded like magic, almost too good to be true. When working with messy data, we are used to making tradeoffs. From statistics, there is the bias-variance tradeoff: you can't improve one without impacting the other.