Comments (4)
Thanks for the suggestion @drawlinson . Currently, you can access the estimator after each estimate_effect call.
effect = model.estimate_effect(...)
effect.estimator # returns the fitted CausalEstimator estimator object
Does the above code provide one way to address the issue? That said, I do see the convenience and clarity of your proposal for the end-user. It can be used to compare the different estimators on the same causal model. Feel free to issue a PR and I can review.
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I hadn't spotted the estimator object there! In fact I'd used that in the past, I just forgot about it. I will make a PR but the method you've pointed out does solve the problem, so the change I'm proposing is only a convenience.
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If this is added, I could also modify an example to extract model coefficients and show how we can explore them in a plot.
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Apologies for delay, PR here: #1113
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Related Issues (20)
- how to use the function of estimate_effect of CausalModel class? HOT 4
- gcm.arrow_strength providing different ranking HOT 6
- conda release 0.11.1 is missing HOT 1
- File Not Found error in Jupyter Notebook when i use auto.assign_causal_mechnism HOT 8
- buggidy giggidy
- Counterfactual Reasoning with Categorical Variables HOT 3
- Time complexity of constructing cause-and-effect diagram HOT 3
- Support polars data frames HOT 2
- What is the purpose of providing observation data in gcm.conventional_samples()?
- Python 3.12 support HOT 3
- Clarify the differences among refute methods HOT 9
- Feature relevance/Influence HOT 22
- Graphviz installation : --include-path not recognized anymore HOT 4
- Does this package support non-English languages? HOT 3
- Question about Dummy Outcome Refuter HOT 2
- Inconsistency in the placebo_treatment_refuter when using estimate_effect of IV HOT 1
- numpy.dual is dropped but it still occurs in dowhy HOT 2
- NetworkXError: graph should be directed acyclic HOT 2
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