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Active learning and decision making with varying treatment effects!

In a new paper, Iiris Sundin, Peter Schulam, Eero Siivola, Aki Vehtari, Suchi Saria, and Samuel Kaski write:

Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based decision-making in a task of deciding which action a to take for a target unit after observing its covariates x̃ and predicted outcomes p̂(ỹ∣x̃,a). An example case is personalized medicine and the decision of which treatment to give to a patient. A common problem when learning these models from observational data is imbalance, that is, difference in treated/control covariate distributions, which is known to increase the upper bound of the expected ITE estimation error. We propose to assess the decision-making reliability by estimating the ITE model’s Type S error rate, which is the probability of the model inferring the sign of the treatment effect wrong. Furthermore, we use the estimated reliability as a criterion for active learning, in order to collect new (possibly expensive) observations, instead of making a forced choice based on unreliable predictions. We demonstrate the effectiveness of this decision-making aware active learning in two decision-making tasks: in simulated data with binary outcomes and in a medical dataset with synthetic and continuous treatment outcomes.

Decision making, varying treatment effects, type S errors, Stan, validation. . . this paper has all of my favorite things!


  1. Don’t forget causality, propensity scoring, automatic relevance determination, and lots of definitions, theorems, and proofs!

    The abstract says that type-S error is “the probability of the model inferring the sign of the treatment effect wrong.” Is this something that only makes sense in a causal context? What does it mean to infer the sign of a treatment effect? Is it something like bounding the posterior away from zero like in a NHST? What’s the difference between 0 + epsilon and 0 – epsilon as an estimate in a regression? Don’t they both have roughly no effect?

    I tried to read the paper, but got lost in the examples, definitions, and theorems.

    • Sam says:

      My understanding is that the `difference between 0 + epsilon and 0` is you’d give different treatments to a particular person. Suppose you had a treatment and a control and found the individual treatment effect for a particular person is + epsilon. Then you should give the person the treatment. I can see this making a difference if you want to get an ‘accuracy correct treatment’ stat or something like that but seems irrelevant for really small ITEs.

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