Frequently Asked Questions (FAQ)

When should I use EconML?

EconML is designed to answer causal questions: what will happen in response to some change in behavior, prices, or conditions? These questions require different methods than forecasting questions: what will happen next if everything continues as it has been?

What are the advantages of EconML?

EconML offers the broadest range of cutting-edge AI models designed specifically to answer causal questions. The EconML models also build on familiar Python packages, allowing users to easily select the best model for their question. Finally, EconML includes custom interpreters to create presentation-ready output.

How do I know if the results make sense?

Try comparing the consistency of your estimates across multiple models, including some that make stronger structural assumptions like linear relationships and some that do not. Pay attention to the standard errors as well as the point estimates—imprecise estimates should be interpreted accordingly. While researchers can introduce bias by narrowly fishing for estimates that match their prior, it is also important to use your expertise to evaluate results. If you estimate that a 5% decrease in price generates an implausible 5000% increase in sales you should carefully review your code!

I’m getting causal estimates that don’t make sense. What next?

First carefully check your code for errors and try several causal models. If your estimates are consistent, but implausible, you may have a confounding variable that hasn’t been measured in your data. Think carefully about the source of the data you are using: was there something unusual going on during the period when the data were collected (for example a holiday or an economic downturn)? Is there something unusual about your sample (for example, all men with pre-existing heart conditions)?

What if I don’t have a good instrument, can’t run an experiment, and don’t observe all confounders?

In this case, no statistical approach can perfectly isolate the causal effect of the treatment on the outcome. DML, OrthoForest, or MetaLearners, all including all the confounders you can observe, will deliver the best approximation of the causal effect that minimizes the bias from confounders. Be aware of some remaining bias when using these estimates.

How can I test whether I’m identifying the causal effect?

You are identifying a valid causal effect if and only if the underlying assumptions of the causal model assumed by the estimation routine are correct. Those are often hard to test (though the DoWhy package may help). Having made those assumptions, the EconML package allows you to fit the best causal model you can. Many models will store a final stage fit metric that can be used to validate how well the causal model predicts out of sample, which is a good diagnostic as to the quality of your model.

How do I give feedback?

This project welcomes contributions and suggestions. We use the DCO bot to enforce a Developer Certificate of Origin which requires users to sign-off on their commits. This is a simple way to certify that you wrote or otherwise have the right to submit the code you are contributing to the project. Git provides a -s command line option to include this automatically when you commit via git commit.

This project has adopted the PyWhy Code of Conduct.