causal inference

Intromental variables (IV) is an alternative causal inference method that does not rely on the ignorability assumption.

In this post we will continue on discussing the estimate of causal effects. We will talk about intuition of IPTW, some key definitions like weighting, marginal structual models. And in the end we will show a data example in R.

In the Part 1 we talked about the basic concepts of causal effect and confounding. In this post we will proceeed with discussing about how to control the confounders with matching.

Causal inference has been a heated field in statistics. It has great application for observational data. In this post I will shares some key concepts of causal inference:

• The confusion over causal inference
• The important causal assumptions
• The concept of causal effects
• Confounding and Directed Acyclic Graphs