Counterfactual Evaluation and Learning

I came across this very interesting talk by some folks at Cornell on Counterfactual Evaluation.

Some thoughts:

• Systems which deal with a lot of offline evaluation might benefit a lot if they log they probability score when choosing the best action, because it would enable them to compute the IPS scores.
• Counterfactual evaluation deals with the offline scenario.  There are 2 primary parts to it, both of which I think go hand in hand.
  • Evaluation of a given policy
    • IPS seems to be a very attractive measure for counterfactual evaluation, because it produces an unbiased estimate of the utility function.
  • Learning the best policy
  • For both evaluation and learning, the standard approach would be to model the reward. aka a reward predictor.
    • I have to admit the reward predictor approach is much more intuitive for me.
  • The approach proposed by Joachim’s et al. is how to do better.
    • For evaluation, they propose “modeling a bias” approach using IPS as the evaluation metric.
    • For learning, they use the AMO (“arg-max oracle”) approach i.e. reduce the problem of finding the best policy to a weighted multi-class classification problem. In a previous post I had mentioned about this reduction which is implemented in the VW library.

• For online settings, the contextual bandit problem can be solved using Epsilon Greedy / Epoch Greedy.
  • Schapire’s video explains this and proposes a new algorithm to solve it  with better regret bounds, and fewer calls

References:

• http://www.cs.cornell.edu/~adith/CfactSIGIR2016/
  • Conterfactual Evaluation. Mostly deals with evaluation and learning of policies in offline scenarios.
• https://www.youtube.com/watch?v=gzxRDw3lXv8
  • Discusses an approach to solve the contextual bandit problem in the online setting. Provides a good overview of the conextual bandits problem.
• http://research.microsoft.com/en-us/um/cambridge/events/mls2013/downloads/counterfactual_reasoning.pdf

 

Code:

• evaluator_ips

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