Structural equation modeling uses causal priors to structure an equation. The parameters of that equation are optimized using machine learning techniques.
this post was submitted on 17 Nov 2023
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Honestly I'm not sure what you are really asking. Could you define Machine Learning in the way you see it? I feel like the answer is too obvious to be the answer you're looking for.
I guess my point is, causal inference has been around for a long time whereas Causal ML just popped up a few years ago. So how is Causal ML different from Causal inference. What are the unique problem Causal ML tries to solve that Causal inference couldn't?
Learning causal effects from observational data is not straightforward, because of two reasons:
- you have strong confounder bias: treatments are not assigned at random (unlike in an active experiment setup like an RCT). Patients who are more sick will receive more treatment. And
- you never observe counterfactuals (if you treat a patient, you do not observe what would have happened if you had not treated this patient).
ML can help tackling the first issue by learning proper representations for your treated and non-treated groups. Simplified: if you manage to find representations for your control and treatment groups so that the distributions of your representations (which contain your confounding factors), you can make unbiased treatment effect estimates.
The second issue can be solved by using (semi-) synthetic data.
Traditional casual inference was focused on cases where any confounding was caused by a known set of linear confounders. In this case, straightforward application of linear regression or other GLMs can be used to produce causal estimates. If you have a huge number of potential confounders or if you are worried about non linear terms or interactions between confounders, then those traditional approaches do not work.
Causal ML approaches have worked out the theory to allow you to use flexible machine learning estimators to remove the impact of these other variables so that you can get an unbiased estimate of the causal effect of your treatment variable.