resolves #796 remove dep on pygam, use IsotonicRegression

[ras44]

Proposed changes

This removes the dependency on pygam and replaces the use of LogisticGAM() in causalml/propensity.py : calibrate() with IsotonicRegression(). Work shown here supports that using IsotonicRegression() produces comparable or improves log-loss and brier score loss values.

This removes the pygam dependency, which is acting as a constraint on other dependencies.

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[jeongyoonlee]

Thanks for the quick turnaround, @ras44. The test is failing, I guess, because the new calibrated propensity contains 0 or 1. Propensity scores, or treatment probabilities, should be within (0, 1), excluding 0 or 1, and binomial-like outputs are not ideal.

[jeongyoonlee]

I'm reposting my message from Slack. ICYMI: In your example notebook, treatment is supposed to be assigned randomly with a 75% probability. So, the logistic regression propensity and PyGAM calibrated scores look better than other calibrated scores, which show binomial distributions.

For a more interesting test, we should try a propensity model, of which outputs are not probabilities, e.g., GBM or RF, and then see how PyGAM calibration changes the distribution and find the calibration method that generates similar outputs.

A simple logistic regression - or sigmoid transformation might work better.

[ras44]

My apologies! Too many windows open and notifications turned off :) Will take a more detailed look at it and hopefully make it to the call tomorrow 👍