[spywaregorilla]

> The DoubleML method is founded on machine learning modeling and consists of two key steps. First, we build a model that predicts the treatment variable based on the input variables . Then, we create a separate model that predicts the outcome variable using the same set of input variables . Subsequently, we calculate the residuals from the former model and regress them against the residuals from the latter model. An important feature of this method is its flexibility in accommodating non-linear models, which allows us to capture non-linear relationships — a distinctive advantage of this approach.

Just... don't do this. You're not going to be able to math your way to better conclusions. Make your model, make your plots, and use critical thinking to evaluate your results.

[morelandjs]

Not sure what point you are trying to make here. Double ML is a valid approach for debiasing confounding effects.

[spywaregorilla]

I disagree. It's vulnerable to all sorts of mishaps. You're now having to worry about data leakage between your treatment group AND your target variable. Casual inference without experiment data is all just a mathematical exercise to make a one size fits all approach to identifying relationships. Yes, correlation has weaknesses. But the name "causal inference" is grossly misleading. It's "well if we assume X, Y, and Z then the effect which we have already assumed is causal is probably around this order of magnitude". And hey, maybe that will help you identify cases where a confounding variable is actually the thing that matters. But you're not going to do better than just doing an analysis on the variables and their interactions. You don't have the brainpower to do this at a scale larger than pretty much all causal methods will begin to fail. It does not offer you the legitimacy the name implies.

I think it confuses far more than it helps.