Comments (2)
Thanks for your interest. The library laurencium/causalinferece also implements causal inference methods, but it focuses only on one step in causal analysis: estimation. In building DoWhy, therefore, we decided to have support for all the steps required for a causal analysis:
- model (make assumptions),
- identify (find what to estimate given the assumptions),
- estimate
- refute (sensitivity and robustness checks).
I think the biggest difference is the last step: DoWhy provides refute methods to test underlying causal assumptions. This is often missed in causal inference libraries that only deal with estimation, as the user is expected to know beforehand what is the right causal target estimand, and which covariates to include. DoWhy also allows the user to specify your asssumptions explicitly in code, so that they can be reasoned about and tested if possible.
Finally, in terms of estimation methods, DoWhy supports both backdoor-based and instrumental variable methods, while laurencium/causalinference library supports only backdoor-based methods.
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@amit-sharma Sounds awesome! Thank you for taking time to answer my question. I'll definitely delve more into the inner workings in the next couple of days. I'm closing this issue in hope to keep things clean since your answer is really thorough.
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Related Issues (20)
- NetworkXError: graph should be directed acyclic HOT 4
- Refutation & Overlap Error ("data_subset_refuter", "add_unobserved_common_cause", assess_support_and_overlap_overrule) HOT 2
- No Backdoor Path Available
- Clarification on how to use gcm properly for confounders adjustment HOT 5
- Can you provide code demo for each function? HOT 2
- How is propensity score matching implemented? HOT 2
- Interpreting mean while using logistic regression to estimate causal effect. HOT 1
- model.estimate_effect and model.refute_astimate throws 'A column-vector y was passed ...' error
- RuntimeWarning: divide by zero encountered in divide when using evaluate_causal_model HOT 5
- Auto assign_causal_mechanisms is taking so much time in gcm HOT 12
- falsify_graph HOT 8
- Remove use of CausalModel from test files and notebooks
- Is there a way to work with a structural causal model where nodes have multiple values? For use in root cause analysis. HOT 8
- graph should be directed acyclic HOT 4
- dowhy replication issue and output discrepancy with statsmodels HOT 2
- how to show summary stats (R squared, p value, F stat) from causal estimate HOT 3
- dowhy 0.11.1 dependences looks not being loaded HOT 2
- Creating CausalModel with frontdoor variables HOT 2
- Sensitiviy Analysis for Binary Outcome HOT 1
- 'squared' is deprecated error while running evaluate_causal_model HOT 2
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