Causal inference

5 important questions on Causal inference

What is causal inference and how do we show it?

It is estimating causal relationships.

  • By drawing lines between causes and effects

How do show the causal effect?

Yi(a=1) IS NOT Yi (a=0)





Y = outcome
a = treatment
1 = yes (received treatment / experienced improvement)
0 = no
i = individuall

What is a counterfactual outcome?

Potential outcome that is not observed because the subject did not experience the treatment (counter the fact)
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Under which three conditions can average causal effects be determined?

  1. Positivity
  2. Consistency
  3. Exchangeability

What is positivity & positive probability?

Positivity is the assumption that any individual has a positive probability of receiving all values of the treatment variable. In other words, you need to have people that do receive any form of intervention or have certain characteristics and people to compare them to (controls). There have to be people in all treatment arms.

Positive probability is that there are people in each group.
  • There needs to be a positive probability of everyone receiving the treatment.

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