Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Judith
Lok*, Associate Professor of Biostatistics at Harvard University's TH Chan
School of Public Health. She will be presenting work entitled *Defining and
estimating causal direct and indirect effects when setting the mediator to
specific values is not feasible.* Please find the abstract below and on
the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
.
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: Defining and estimating causal direct and indirect effects when
setting the mediator to specific values is not feasible
Natural direct and indirect effects decompose the effect of a treatment
into the part that is mediated by a covariate (the mediator) and the part
that is not. Their definitions rely on the concept of outcomes under
treatment with the mediator ``set'' to its value without treatment.
Typically, the mechanism through which the mediator is set to this value is
left unspecified, and in many applications it may be challenging to fix the
mediator to particular values for each unit or individual. Moreover, how
one sets the mediator may affect the distribution of the outcome. This
presentation introduces ``organic'' direct and indirect effects, which can
be defined and estimated without relying on setting the mediator to
specific values. Organic direct and indirect effects can be applied for
example to estimate how much of the effect of some treatments for HIV/AIDS
on mother-to-child transmission of HIV-infection is mediated by the effect
of the treatment on the HIV viral load in the blood of the mother.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Peng
Ding*, Postdoctoral Fellow in Statistics at Harvard University, and
Assistant Professor of Statistics at Berkeley beginning January 2016. He
will be presenting work entitled *Sensitivity Analysis Without Assumptions.*
Please find the abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>
<http:>.
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: Sensitivity Analysis Without Assumptions
Unmeasured confounding may undermine the validity of causal inference with
observational studies. Sensitivity analysis provides an attractive way to
partially circumvent this issue by assessing the potential influence of
unmeasured confounding on the causal conclusions. However, previous
sensitivity analysis approaches often make strong and untestable
assumptions such as having a confounder that is binary, or having no
interaction between the effects of the exposure and the confounder on the
outcome, or having only one confounder. Without imposing any assumptions on
the confounder or confounders, we derive a bounding factor and a sharp
inequality such that the sensitivity analysis parameters must satisfy the
inequality if an unmeasured confounder is to explain away the observed
effect estimate or reduce it to a particular level. Our approach is easy to
implement and involves only two sensitivity parameters. Surprisingly, our
bounding factor, which makes no simplifying assumptions, is no more
conservative than a number of previous sensitivity analysis techniques that
do make assumptions. Our new bounding factor implies not only the
traditional Cornfield conditions that both the relative risk of the
exposure on the confounder and that of the confounder on the outcome must
satisfy, but also a high threshold that the maximum of these relative risks
must satisfy. Furthermore, this new bounding factor can be viewed as a
measure of the strength of confounding between the exposure and the outcome
induced by a confounder.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
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…>
.
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
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Marc
Ratkovic *and *Dustin Tingley*. Marc is an Assistant Professor of Politics
at Princeton University, and Dustin is a Professor of Government at Harvard
University. They will be presenting work entitled *Sparse Estimation and
Uncertainty with Application to Subgroup Analysis*. Please find the
abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations…>.
Additionally, the paper is available here
<http://scholar.harvard.edu/files/dtingley/files/sparsereg.pdf>.
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: Sparse Estimation and Uncertainty with Application to Subgroup
Analysis
Abstract: We introduce a Bayesian method, LASSOplus, that unifies recent
contributions in the sparse modeling literatures, while substantially
extending upon pre-existing estimators in terms of both performance and
flexibility. Unlike existing Bayesian variable selection methods, LASSOplus
both selects and estimates effects, while returning estimated confidence
intervals among discovered effects. Furthermore, we show how LASSOplus
easily extends to modeling repeated observations, and permits a simple
Bonferroni correction to control coverage on confidence intervals among
discovered effects. We situate the LASSOplus in the literature on exploring
sub-group effects, a topic that often leads to a proliferation of
estimation parameters. We also offer a simple pre-processing step that
draws on recent theoretical work to estimate higher-order effects that can
be interpreted independent of their lower-order terms. A simulation study
illustrates the method’s performance relative to several existing variable
selection methods. Application to an existing study of support for climate
treaties illustrates the method’s ability to discover substantively
relevant effects. Software implementing the method is made publicly
available in the R package *sparsereg*.
--
Aaron R Kaufman
PhD Candidate, Harvard University
Department of Government
818.263.5583
