Wrapping it up!
In this chapter, we introduced several methods and ideas that aim to overcome the limitations of traditional causal discovery frameworks. We discussed DECI, an advanced deep learning causal discovery framework, and demonstrated how it can be implemented using Causica, Microsoft’s open source library, and PyTorch.
We explored the FCI algorithm, which can be used to handle data with hidden confounding, and introduced other algorithms that can be used in similar scenarios. These methods provide a strong foundation for tackling complex causal inference problems.
After that, we discussed two frameworks, ENCO and ABCI, that allow us to combine observational and interventional data. These frameworks extend our ability to perform causal discovery and provide valuable tools for data analysis.
Finally, we discussed a number of challenges that we face when applying causal discovery methods to real-world problems.
We are inexorably approaching the end of our...
