TBD.

One could read Sebastian Ruderâ€™s NN-style introduction to â€śtransfer learningâ€ť. NN people like to think about this in particular way which I like because of the diversity of out-of-the-box ideas it invites and which I dislike because it is sloppy. The central idea here is learning well-factored causal graphical models and everything else is just an approximation to that. The reason this is hot topic in neural nets, I suspect, is that it is convenient for massive, low-human-effort neural networks to ignore graphical structure to get good results from regressions in observational data. To recovery the fancy performance in a black-box model is even more tedious than a classical one. Also it fits the social conventions of neural network research to reinvent methods to fix such problems without reference to previous conventions, for better and worse.

One thing that the machine learning set up gives us which is an additional emphasis:
external validity, the traditional framing, would ask you whether the model you have learnt is still useful on new data.
The transfer learning set up invites use to consider if we can *transfer* some of the *computational effort* from learning on one data set to learning on new dataset, and if so, how much.

This connects also to semi-supervised learning and fairness, argues (Bernhard SchĂ¶lkopf Bernhard et al. 2012; Bernhard SchĂ¶lkopf 2019).

### Standard graphical models

We can just try some basic graphical model technology and see how far we get. If the right independences are enforced, presumably we are doing something not too far from learning a transferable model? Or, if we work out that the necessary parameters are not identifiable, then we discover that we cannot in fact learn a transferable model, right? (But maybe we can learn a somewhat transferable model?) I guess the key weakness here is that graphical models will miss some types of transferability, specifically, independences that are dependent on particualr values of the nodes, so this might be less powerful.

## Tools

### Salad

salad is a library to easily setup experiments using the current state-of-the art techniques in domain adaptation. It features several of recent approaches, with the goal of being able to run fair comparisons between algorithms and transfer them to real-world use cases.

## References

*Advances in Neural Information Processing Systems 30*, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, 656â€“66. Curran Associates, Inc. http://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasoning.pdf.

*Frontiers in Big Data*2. https://doi.org/10.3389/fdata.2019.00013.

*Statistical Science*29 (4): 579â€“95. https://doi.org/10.1214/14-STS486.

*Elements of Causal Inference: Foundations and Learning Algorithms*. Adaptive Computation and Machine Learning Series. Cambridge, Massachuestts: The MIT Press. https://www.dropbox.com/s/dl/gkmsow492w3oolt/11283.pdf.

*Dataset Shift in Machine Learning*. Cambridge, Mass.: MIT Press. http://ndl.ethernet.edu.et/handle/123456789/47647.

*ICML 2012*. http://arxiv.org/abs/1206.6471.

*Journal of Economic Methodology*12 (2): 225â€“37. https://doi.org/10.1080/13501780500086081.

*The 22nd International Conference on Artificial Intelligence and Statistics*, 3118â€“27. PMLR. http://proceedings.mlr.press/v89/subbaswamy19a.html.

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