causality
1. Topics under Discussion
---------------------------------
1.1
Principal Stratification - A goal or a tool?'
http://ftp.cs.ucla.edu/pub/stat_ser/r382.pdf
http://escholarship.org/uc/item/4xj9d380#page-3
Was posted for discussion in the International Journal of
Biostatistics (IJB) in March 2011, and has elicited
response from eight discussants on whether
studies based on Principal Stratification
estimate quantities that researchers care about.
I am about to wrap up the discussion with a summary-rejoinder, so,
if you have comments or insights that where not brought up, feel free
to communicate them to the IJB's Editor,
"Nicholas P. Jewell" <mm-11332-3261687@bepress.com>
or/and, if you wish, cross-post them on this blog.
1.2
Comments and Controversies:
We are being warned again that graphical models
can produce "incorrect" causal inferences. The warning
comes again from Lindquist and Sobel (LS),
entitled "Cloak and DAG":
http://www.sciencedirect.com/science/article/pii/S1053811911013085
A response to L&S is posted on our causality blog,
proving them wrong, and questioning the wisdom of asking
researchers to translate assumptions from a language
where they stand out
vividly and meaningfully into an Arrow-Phobic language
where they can no longer be recognized, let alone justified.
We have all the reason to suspect that L&S will come back.
1.3
The Match-Maker Paradox
An apparent paradox concerning the representation
of matching designs in DAGs was posted by Pablo Lardelli
and resolved by noting that matching involves
unit-to-unit interaction and results in "persistent-unfaithfulness."
1.4
An On-going Causal-Inference Discussions on SEMNET
(Structural Equation Modeling Discussion Group
------------------
In the past four months I have spent time
discussing modern approaches to causal inference
with SEM researchers who, by and large, are still
practicing the traditional methods associated
with the acronym "SEM". The discussions are fully documented
and archived on ihttp://bama.ua.edu/archives/semnet.html
Topics include:
a. the causal/statistical distinction.
b. the structural-regressional distinction
c. The residual/disturbance distinction
d. The assumptions conveyed by each structural equation.
e. The counterfactual reading of structural equations
f. What the Mediation Formula tells us about mediation
and policy questions.
g. Mediators and Moderators.
h. d-separation, equivalent models and the testable
implications of structural models
i. The logic of SEM as an inference engine.
Additionally, a weekly session is being conducted by
Les Hayduk, going page by page over the R-370 chapter
(http://ftp.cs.ucla.edu/pub/stat_ser/r370.pdf)
and explaining it to novices in the field.
It answers, I hope, all questions that
rank and file researchers find perplexing when
introduced to causal analysis.
1.5 Draft Chapter on Causality and SEM
Ken Bollen and I finished a draft chapter titled
"Eight Myths about Causality and Structural Equation Models."
It covers the history of misconceptions about
SEM, including recent assaults by the Arrow-Phobic Society.
see
http://ftp.cs.ucla.edu/pub/stat_ser/r393.pdf
1.6 A Survey Paper on Adjustment
Greenland, S., and Pearl J., "Adjustments and their Consequences --
Collapsibility Analysis using Graphical Models"
http://ftp.cs.ucla.edu/pub/stat_ser/r369.pdf
The paper teaches researchers how to glance at
a graph and determine when/if an adjustment for
one variable modifies the relationship between
two other variables. It is a simple exercise for graphical
modellers but extremely difficult one for economists
and other researchers who ask such questions routinely
and have no graphs for guidance.
1.7 A New Introduction to Causal Calculus
An excellent introduction to causal diagrams
and do-calculus was posted recently by
bloggist-author Michael Nielsen, titled
"If correlation doesn't imply causation, then what does?"
It can be accessed here:
http://www.michaelnielsen.org/ddi/if-correlation-doesnt-imply-causation-then-what-does/
My response, together with thoughts on the psychology of
Simpson's Paradox is below
1.8 Haavelmo and the Emergence of Causal Calculus
--------------------------
http://ftp.cs.ucla.edu/pub/stat_ser/r391.pdf
Presented at Haavelmo Centennial Symposium, in Oslo, last
December, the paper describes the cultural barriers that Haavelmo's
ideas have had to overcome in the past six decades and
points to the fact that modern economists are still unaware of
the benefits that Haavelmo's ideas bestow upon them.
2. New Results in Causal Inference
--------------------
2.1 Interpretable Conditions for Identifying Natural Direct
Effects. http://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf
The paper lists four conditions that are sufficient for the
identification of natural direct and indirect effects.
The conditions do not invoke "ignorability"
jargon thus permitting more informed judgment
of the plausibility of the assumptions.
It also shows that conditions usually
cited in the literature are overly restrictive,
and can be relaxed without compromising identification.
2.2 Some Thoughts on Transfer Learning, with Applications to
Meta-analysis and Data-sharing Estimation
http://ftp.cs.ucla.edu/pub/stat_ser/r387.pdf
Summary: How to combine data from multiple
and diverse environments so as to take full advantages
of that which they share in common.
2.3 Understanding Bias Amplification
http://ftp.cs.ucla.edu/pub/stat_ser/r386.pdf
This note sheds a new light on the phenomenon
of "bias amplification" by considering the cumulative
effect of conditioning on multiple "near instruments,"
and shows that bias amplification may build up at
a faster rate than bias reduction.
2.4 Local Characterizations of Causal Bayesian Networks
by Bareinboim, Brito and Pearl
http://ftp.cs.ucla.edu/pub/stat_ser/r384.pdf
The standard definition of Causal Bayesian Networks
(CBN) requires that every interventional distribution
be decomposable into a truncated product, dictated by the graph.
This paper replaces this "global" definition with three
alternative ones, each invoking "local" aspects of
conditioning and intervening.
3.
Journals, Courses, Lectures and Conferences,
==========================================
3.1
Tutorial: Causal Inference in Statistics
--------------------------------------
I will be giving a tutorial (IOL) on causal inference at
the upcoming JSM 2012 conference in San Diego
California, July 29th, 4-5:50 PM.
If any of your students or colleagues wishes to attend,
the abstract and further details can be found on
http://www.amstat.org/meetings/jsm/2012/onlineprogram/AbstractDetails.cfm?abstractid=304318
3.2 The Journal of Causal Inference
------------------------------
As reported in the last blog email, the Journal
of Causal Inference (JCI) was launched on September
2010 and the website is open for submissions.
The first issue is planned for Summer of 2012 and,
needless to state, you are invited to submit your latest results,
and to bring JCI to the attention of students and colleagues
who might be seeking a forum for presenting their latest
ideas, results and, yes, breakthroughs!
The Journal of Causal Inference will highlight both the
uniqueness and interdisciplinary nature of causal research.
and will publish both theoretical and applied research
including survey and discussion papers.
3.3
Spring Workshop Graphical Causal Models
------------------------------------
Friday 3/30/2012
Northwestern University, Chicago
This workshop will introduce graphical causal models,
show how to simulate data from, and estimate such
models in Tetrad, explain model search, and more....
Lecturers: Richard Scheines and Joseph Ramsey CMU
For details see chicagochapterasa@gmail.com
http://community.amstat.org/Chicago_Chapter/Calendar/20112012/NewItem6/
3.4
Conference: EVIDENCE AND CAUSALITY IN THE SCIENCE
ECitS 2012
Centre for Reasoning, University of Kent, 5-7 September 2012
Organizers: Phyllis Illari and Federica Russo
http://www.kent.ac.uk/secl/philosophy/jw/2012/ecits/
3.5
New Software tool for Causal Inference
------------------------------------
DAGitty: A Graphical Tool for Analyzing Causal Diagrams
by Textor, Johannes; Hardt, Juliane; Knüppel, Sven
Epidemiology:
September 2011 - Volume 22 - Issue 5 - p 745
doi: 10.1097/EDE.0b013e318225c2be
This paper announces the release of DAGitty,
a graphical user interface for drawing
and analyzing causal diagrams. DAGitty, offers several
improvements over Kyono's "COMMENTATOR"
http://ftp.cs.ucla.edu/pub/stat_ser/r364.pdf
among them efficient listing of all minimal sufficient adjustment
sets. It is available under an open-source license
obtained at www.dagitty.net and www.dagitty.net/manual.pdf
Best wishes