13. Causal Inference

13.1 Introduction

We’ve introduced a couple of machine-learning algorithms and suggested that they can be used to produce clear, interpretable results. You’ve seen that logistic regression coefficients can be used to say how much more likely an outcome will occur in conjunection with a feature (for binary features) or how much more likely an outcome is to occur per unit increase in a variable (for real-valued features). We’d like to make stronger statements. We’d like to say “If you increase a variable by a unit, then it will have the effect of making an outcome more likely.”

These two interpretations of a regression coefficient are so similar on the surface that you may have to read them a few times to take away the meaning. ...

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