I am quite new to the topic and trying to figure out a workflow for causal analysis. My aim is to establish a baseline of ATE (I think) and then experiment with disentangled representations and machine learning (e.g CEVAE). I am mainly going to use the Lalonde [A] dataset and the IHDP [B] simulated dataset by Hill and try to re-implement CEVAE. My questions towards folks who have an existing workflow and use causality for their research are as below:
- In studies where one only has observational data and the causal graph is intractable (is it?) are ATE, ATT and ATC the goals of the analysis to estimate causal effects?
- In order to compute the above metrics I need to find a way to compute counterfactuals. Does the following approach make sense: a) identify a method of counterfactual estimation (e.g linear regression) b) compute counterfactuals c) calculate metrics (e.g ATE) ?
- If so can you suggest examples with code (python preferably) and other methods more suitable than linear regression?
- Finally, the DoWhy library suggest an extra step of refutation? How do I verify that my counterfactual estimation method is robust? What is the theory? Many repetitions?
I have already looked at several tutorials and packages like DoWhy (Example with Lalonde dataset-DoWhy) and what baffles me is the fact that no one seems to explicitly compute counterfactuals and use them as an estimated quantity for following experiments. Also, this question seems relevant but does not really answer mine: Using counterfactual modeling techniques to assess racial bias in predictive models . I would be grateful if anyone could suggest worked examples rather than plain theory or Pearl's book. Thank you.
[A] R. J. LaLonde. Evaluating the econometric evaluations of training programs with experimental data. The American economic review, pages 604–620, 1986.
[B] J. L. Hill. Bayesian nonparametric modeling for causal inference. Journal of Computational and Graphical Statistics, 20(1):217–240, 2011.