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.
from dowhy.
Related Issues (20)
- Clear documentation on identification methods HOT 2
- Backdoor path HOT 4
- Linear dataset functionality and parameters HOT 1
- Simple constraints for the SCM HOT 1
- How can I get more log messages from dowhy? HOT 4
- Identify effect not showing backdoor variable HOT 5
- numpy has no attribute 'long' HOT 1
- No common causes/confounders present. HOT 3
- Сausal effect for non-linear relationship HOT 1
- Compatability with networkx is broken HOT 8
- Continuous Treatment Variable HOT 1
- CausalEstimator reporting a 90% instead of 95% confidence interval for bootstrapping? HOT 5
- Hanging when refuting right after calculating confidence interval HOT 2
- Incomplete `method_name` argument documentation in `estimate_effect` HOT 4
- Add accessor to CausalModel._estimator_cache HOT 4
- Evaluation Metrics for Causal Graphs HOT 4
- Inconsistent encoding with pandas get_dummies causes prediction and effect estimation errors HOT 6
- Falsification of given DAG: not working on simulated data? HOT 4
- Causal Graph not provided. DoWhy will construct a graph based on data inputs. HOT 1
- how to use the function of estimate_effect of CausalModel class? HOT 4
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