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Hi, hank you for releasing this work and all the effort put in to it.
I was curious to understand what are the primary differences compared to this library? Are there any pros and cons?
Best,
k.
The text was updated successfully, but these errors were encountered:
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.
kirk86
changed the title
how does it comparet to causalinference library
how does it compare to causalinference library
Aug 24, 2018
@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.
Hi, hank you for releasing this work and all the effort put in to it.
I was curious to understand what are the primary differences compared to this library? Are there any pros and cons?
Best,
k.
The text was updated successfully, but these errors were encountered: