[otterk10]

Yes, you're correct that the underlying algorithm used is very close to OLS. What allows the regression to provide an estimate for average treatment effects is how it is structured. Namely, adding in pre-treatment confounders as well as interactions between the treatment and confounders. I found this chapter (http://www.stat.columbia.edu/~gelman/arm/chap9.pdf) on causal inference does a good job of outlining the approach.

Yes, we actually explored other approaches such as PSM. The main reason we did not initially go with PSM was because of the compute power required - you would need to train a model for each treatment variable. However, we're actually in the midst of developing a way to train a model for each treatment variable efficiently, which will allow us to add items such as inverse propensity weighting (or explore other approaches such as PSM).

[lern_too_spel]

This approach only works if all confounders are known, which is never the case in practice, so the model you fit is correlational and not suitable for causal inference. Propensity matching suffers from the same issue if the propensities are estimated from the same features. If not all confounders are known, you must be able to find instrumental variables to build a causal model.

[cuchoi]

Thanks for answering!