[gov3009-l] Applied Statistics - 10/14 - Cynthia Rudin

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Oct 12 11:20:29 EDT 2015


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming* Cynthia
Rudin*, Associate Professor of Statistics at MIT where she runs the
Prediction Analysis Lab. She will be presenting work entitled* A Machine
Learning Perspective on Causal Inference*.  Please find the abstract below
and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/peng-ding-harvard-sensitivity-analysis-without>
.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.

-- Aaron Kaufman

Title: A Machine Learning Perspective on Causal Inference

Abstract: Usually the terms "causal inference" and "machine learning" mix
like oil and water. Machine learning models are often black box complicated
functions that provide predictions without causal explanations. For causal
inference, this kind of model is unacceptable. Maybe we can find ways to
harness the predictive power of machine learning methods for the purpose of
causal inference. I will discuss three very recent preliminary ideas, from
the perspective of a machine learner:

1) Causal Falling Rule Lists (with Fulton Wang). This is a machine learning
method that bridges the gap - it's nonlinear yet interpretable, and models
causal effects. (More details below.)

2) The Factorized Self-Controlled Case Series Method: An Approach for
Estimating the Effects of Many Drugs on Many Outcomes (with Ramin
Moghaddass and David Madigan). We estimate the effects of many drugs on
many outcomes simultaneously. This Bayesian hierarchical model is
formulated with layers of latent factors, which substantially helps with
both computation and interpretability.

3) Robust Testing for Causal Inference in Natural Experiments (with Md.
Noor-E-Alam). We claim there is a major source of uncertainty that is
ignored in matched pairs tests, which is how the matches were constructed
by the experimenter. No matter which reasonably good experimenter conducts
the test, the hypothesis test result still ought to hold. Our robust
matched pairs tests use mixed-integer programming.

----- (More on Causal Falling Rule Lists) ----

A Causal Falling Rule List is a sequence of IF-THEN rules that specifies
heterogeneous treatment effects. In this model, (a) the order of rules
determines the treatment effect subgroup that a subject belongs to, (b) the
treatment effect decreases monotonically down the list.

For example, a Causal Falling Rule List might say that if a person is below
60 years, then they are in the highest treatment effect subgroup, such that
administering a drug will result in a 20 unit increase in good cholesterol
levels. Otherwise, if they are regular exercisers, then taking the drug
will result in a 15 unit increase in cholesterol level. Finally, if they
satisfy neither of these rules, they are in the default treatment subgroup,
such that the drug will result in only a 2 unit increase.

The collection of rules, their sequence, and the treatment effects are
learned from data.

----------

-- 
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
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