Discover and read the best of Twitter Threads about #causalinferencebook

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Are you still shaking off the holiday? I know I am!

How about a #cartooncausalinference #tweetorial about casual graphs to ease us into the new year?

#epitwitter #DAGsfordocs #FOAMed #MedEd #statstwitter #econtwitter
The most common type of causal graph (at least on #epitwitter) is the directed acyclic graph, or #DAG.

DAGs have two main components: variables (also called nodes), and arrows (also called edges).

In the DAG below, there are 3 variables: sleeping, Santa, and presents.
The variables are ordered based on time — you have to go to sleep before Santa can come to your house & then he’ll leave presents!

Causation and time both flow in the direction of the arrows.
Read 24 tweets
The results of my #tweetorial poll were pretty clear: from over 350 votes, 38% of you want to know about causal survival analysis. So, pull up a chair and let’s talk time-to-event!
First things first, what do we mean by causal survival analysis?

An answer to the question: How would the average time to event have differed if everyone had received some exposure, versus if everyone had received some other exposure?
We can measure that difference between in time to event in many different ways. I prefer survival curves with risk differences at the end of follow-up, adjusting for baseline and time-varying confounding.
Read 33 tweets

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