Causal Analysis in Theory and Practice

February 22, 2017

Winter-2017 Greeting from UCLA Causality Blog

Filed under: Announcement,Causal Effect,Economics,Linear Systems — bryantc @ 6:03 pm

Dear friends in causality research,

In this brief greeting I would like to first call attention to an approaching deadline and then discuss a couple of recent articles.

1.
Causality in Education Award – March 1, 2017

We are informed that the deadline for submitting a nomination for the ASA Causality in Statistics Education Award is March 1, 2017. For purpose, criteria and other information please see http://www.amstat.org/education/causalityprize/ .

2.
The next issue of the Journal of Causal Inference (JCI) is schedule to appear March, 2017. See https://www.degruyter.com/view/j/jci

MY contribution to this issue includes a tutorial paper entitled: “A Linear ‘Microscope’ for Interventions and Counterfactuals”. An advance copy can be viewed here: http://ftp.cs.ucla.edu/pub/stat_ser/r459.pdf
Enjoy!

3.
Overturning Econometrics Education (or, do we need a “causal interpretation”?)

My attention was called to a recent paper by Josh Angrist and Jorn-Steffen Pischke titled: “Undergraduate econometrics instruction” (A NBER working paper) http://www.nber.org/papers/w23144?utm_campaign=ntw&utm_medium=email&utm_source=ntw

This paper advocates a pedagogical paradigm shift that has methodological ramifications beyond econometrics instruction; As I understand it, the shift stands contrary to the traditional teachings of causal inference, as defined by Sewall Wright (1920), Haavelmo (1943), Marschak (1950), Wold (1960), and other founding fathers of econometrics methodology.

In a nut shell, Angrist and Pischke  start with a set of favorite statistical routines such as IV, regression, differences-in-differences among others, and then search for “a set of control variables needed to insure that the regression-estimated effect of the variable of interest has a causal interpretation”. Traditional causal inference (including economics) teaches us that asking whether the output of a statistical routine “has a causal interpretation” is the wrong question to ask, for it misses the direction of the analysis. Instead, one should start with the target causal parameter itself, and asks whether it is ESTIMABLE (and if so how), be it by IV, regression, differences-in-differences, or perhaps by some new routine that is yet to be discovered and ordained by name. Clearly, no “causal interpretation” is needed for parameters that are intrinsically causal; for example, “causal effect”, “path coefficient”, “direct effect”, “effect of treatment on the treated”, or “probability of causation”.

In practical terms, the difference between the two paradigms is that estimability requires a substantive model while interpretability appears to be model-free. A model exposes its assumptions explicitly, while statistical routines give the deceptive impression that they run assumptions-free (hence their popular appeal). The former lends itself to judgmental and statistical tests, the latter escapes such scrutiny.

In conclusion, if an educator needs to choose between the “interpretability” and “estimability” paradigms, I would go for the latter. If traditional econometrics education
is tailored to support the estimability track, I do not believe a paradigm shift is warranted towards an “interpretation seeking” paradigm as the one proposed by Angrist and Pischke,

I would gladly open this blog for additional discussion on this topic.

I tried to post a comment on NBER (National Bureau of Economic Research), but was rejected for not being an approved “NBER family member”. If any of our readers is a “”NBER family member” feel free to post the above. Note: “NBER working papers are circulated for discussion and comment purposes.” (page 1).

September 11, 2016

An interesting math and causality-minded club

Filed under: Announcement — bryantc @ 6:08 pm

from Adam Kelleher:

The math and algorithm reading group (http://www.meetup.com/Math-and-Algorithm-Reading-Group/) is based in NYC, and was founded when I moved here three years ago. It’s a very casual group that grew out of a reading group I was in during graduate school. Some friends who were math graduate students were interested in learning more about general relativity, and I (a physicist) was interested in learning more math. Together, we read about differential geometry, with the goal of bringing our knowledge together. We reasoned that we could learn more as a group, by pooling our different perspectives and experience, than we could individually. That’s the core motivation of our reading group: not only are we there to help resolve each other get through the material if anyone gets stuck, but we’re also there to add what else we know (in the format of a group discussion) to the content of the material.

We’re currently reading Causality cover to cover. We’ve paused to implement some of the algorithms, and plan on pausing again soon for a review session. We intend to do a “hacking session”, to try our hands at causal inference and analysis on some open data sets.

Inspired by reading Causality, and realizing that the best open implementations of causal inference were packaged in the (old, relatively inaccessible) Tetrad package, I’ve started a modern implementation of some tools for causal inference and analysis in the causality package in Python. It’s on pypi (pip install causality, or check the tutorial on http://www.github.com/akelleh/causality), but it’s still a work in progress. The IC* algorithm is implemented, along with a small suite of conditional independence tests. I’m adding some classic methods for causal inference and causal effects estimation, aimed at making the package more general-purpose. I invite new contributions to help build out the package. Just open an issue, and label it an “enhancement” to kick of the discussion!

Finally, to make all of the work more accessible to people without more advanced math background, I’ve been writing a series of blog posts aimed at introducing anyone with an intermediate background in probability and statistics to the material in Causality! It’s aimed especially at practitioners, like data scientists. The hope is that more people, managers included (the intended audience for the first 3 posts), will understand the issues that come up when you’re not thinking causally. I’d especially recommend the article about understanding bias https://medium.com/@akelleh/understanding-bias-a-pre-requisite-for-trustworthy-results-ee590b75b1be#.qw7n8qx8d, but the whole series (still in progress) is indexed here: https://medium.com/@akelleh/causal-data-science-721ed63a4027#.v7bqse9jh

June 21, 2016

Spring Greeting from the UCLA Causality Blog

Filed under: Announcement — bryantc @ 3:13 am

Dear friends in causality research,
————————————
This Spring Greeting from UCLA Causality blog contains:
A. News items concerning causality research,
B. New postings, new problems and some solutions.
————————————

A1.
The American Statistical Association (ASA) has announced recipients of the 2016 “Causality in Statistics Education Award”.
http://www.amstat.org/newsroom/pressreleases/05162016_Causality_Award.pdf
Congratulations go to Onyebuchi Arah and Arvid Sjolander who will receive this Award in July, at the 2016 JSM meeting in Chicago.
For details of purpose and selection criteria, see http://www.amstat.org/education/causalityprize/

A2.
I will be giving another tutorial at the 2016 JSM meeting, titled “Causal Inference in Statistics: A Gentle Introduction.”
Details and Abstract can be viewed here: https://www.amstat.org/meetings/jsm/2016/onlineprogram/AbstractDetails.cfm?abstractid=321839

A3. Causal Inference — A Primer
For the many readers who have inquired, the print version of our new book “Causal Inference in Statistics – A Primer” is now up and running on Amazon and Wiley, and is awaiting your reviews, your questions and suggestions. We have posted a book page for this very purpose http://bayes.cs.ucla.edu/PRIMER/, which includes selected excerpts from each chapter, errata and updates, and a sample homework solution manual.

The errata page was updated recently under the diligent eye of Adamo Vincenzo. Thank you Adamo!

The Solution Manual will be available for instructors and will incorporate software solutions based on a DAGitty R package, authored by Johannes Textor.  See http://dagitty.net/primer/

A4.
Vol. 4 Issue 2 of the Journal of Causal Inference (JCI) is scheduled to appear in September 2018. The current issue can be viewed here: http://www.degruyter.com/view/j/jci.2016.4.issue-1/issue-files/jci.2016.4.issue-1.xml My own contribution to the current issue discusses Savage’s Sure Thing Principle and its ramifications to causal reasoning. http://ftp.cs.ucla.edu/pub/stat_ser/r466.pdf

As always, submissions are welcome on all aspects of causal analysis, especially those deemed foundational. Chances of acceptance are inversely proportional to the time it takes a reviewer to figure out what problem the paper attempts to solve. So, please be transparent.

B1.
Recollections from the WCE conference at Stanford.

On May 21, Kosuke Imai and I participated in a panel on Mediation, at the annual meeting of the West Coast Experiment Conference, organized by Stanford Graduate School of Business. http://www.gsb.stanford.edu/facseminars/conferences/west-coast-experiment-conference

Some of my recollections are summarized on our Causality Blog here: http://causality.cs.ucla.edu/blog/index.php/2016/06/20/recollections-from-the-wce-conference-at-stanford/

B2. Generalizing Experimental findings
————————————
In light of new results concerning generalizability and selection bias, our team has updated the “external validity” entry of wikipedia. Previously, the entry was all about threats to validity, with no word on how those threats can be circumvented. You may wish to check this entry for accuracy and possible extensions.

B3. Causality celebrates its 10,000 citations
————————————
According to Google Scholar, https://scholar.google.com/citations, my book Causality (Cambridge, 2000, 2009) has crossed the symbolic mark of 10,000 citations. To celebrate this numerological event, I wish to invite all readers of this blog to an open online party with the beer entirely on me. I dont exactly know how to choreograph such a huge party, or how to make sure that each of you gets a fair share of the inspiration (or beer). So, please send creative suggestions for posting on this blog.

On a personal note: I am extremely gratified by this sign of receptiveness, and I thank readers of Causality for their comments, questions, corrections and reservations which have helped bring this book to its current shape (see http://bayes.ca.ucla.edu/BOOK-2K/)

Cheers,
Judea

June 10, 2016

Post-doc Causality and Machine Learning

Filed under: Announcement — bryantc @ 7:58 am

We received the following announcement from Isabelle Guyon (UPSud/INRIA):

The Machine Learning and Optimization (TAO) group of the Laboratory of Research in Informatics (LRI) is seeking a postdoctoral researcher for working at the interface of machine learning and causal modeling to support scientific discovery and computer assisted decision making using big data. The researcher will work with an interdisciplinary group including Isabelle Guyon (UPSud/INRIA), Cecile Germain UPSud), Balazs Kegl (CNRS), Antoine Marot (RTE), Patrick Panciatici (RTE), Marc Schoenauer (INRIA), Michele Sebag (CNRS), and Olivier Teytaud (INRIA).

Some research directions we want to pursue include: extending the formulation of causal discovery as a pattern recognition problem (developed through the ChaLearn cause-effect pairs challenge) to times series and spatio-temporal data; combining feature learning using deep learning methods with the creation of cause-effect explanatory models; furthering the unification of structural equation models and reinforcement learning approaches; and developing interventional learning algorithms.

As part of the exciting applications we are working on, we will be leveraging a long term collaboration with the company RTE (French Transmission System Operator for electricity). With the current limitations on adding new transportation lines, the opportunity to use demand response, and the advent of renewable energies interfaced through fast power electronics to the grid, there is an urgent need to adapt the historical way to operate the electricity power grid. The candidate will have the opportunity to use a combination of historical data (several years of data for the entire RTE network sampled every 5 minutes) and very accurate simulations (precise at the MW level), to develop causal models capable of identifying strategies to prevent or to mitigate the impact of incidents on the network as well as inferring what would have happened if we had intervened (i.e., counterfactual).Other applications we are working on with partner laboratories include epidemiology studies about diabetes and happiness in the workplace, modeling embryologic development, modeling high energy particle collision, analyzing human behavior in videos, and game playing.

The candidate will also be part of the Paris-Saclay Center of Data Science and will be expected to participate in the mission of the center through its activities, including organizing challenges on machine learning, and help advising PhD students.

We are accepting candidates with background in machine learning, reinforcement learning, causality, statistics, scientific modeling, physics, and other neighboring disciplines. The candidate should have the ability of working on cross-disciplinary problems, have a strong math background, and the experience or strong desire to work on practical problems.

The TAO group (https://tao.lri.fr) conducts interdisciplinary research in theory, algorithms, and applications of machine learning and optimization and it has also strong ties with AppStat the physics machine learning group of the Linear Accelerator Laboratory (http://www.lal.in2p3.fr/?lang=fr). Both laboratories are part of the University Paris-Saclay, located in the outskirts of Paris. The position is available for a period of three years, starting in (the earliest) September, 2016. The monthly salary is around 2500 Euros per month. Interested candidates should send a motivation letter, a CV, and the names and addresses of three referees to Isabelle Guyon.

Contact: Isabelle Guyon (iguyon@lri.fr)
Deadline: June 30, 2016, then every in 2 weeks until the position is filled.

February 12, 2016

Winter Greeting from the UCLA Causality Blog

Friends in causality research,
This greeting from the UCLA Causality blog contains:

A. An introduction to our newly published book, Causal Inference in Statistics – A Primer, Wiley 2016 (with M. Glymour and N. Jewell)
B. Comments on two other books: (1) R. Klein’s Structural Equation Modeling and (2) L Pereira and A. Saptawijaya’s on Machine Ethics.
C. News, Journals, awards and other frills.

A.
Our publisher (Wiley) has informed us that the book “Causal Inference in Statistics – A Primer” by J. Pearl, M. Glymour and N. Jewell is already available on Kindle, and will be available in print Feb. 26, 2016.
http://www.amazon.com/Causality-A-Primer-Judea-Pearl/dp/1119186846
http://www.amazon.com/Causal-Inference-Statistics-Judea-Pearl-ebook/dp/B01B3P6NJM/ref=mt_kindle?_encoding=UTF8&me=

This book introduces core elements of causal inference into undergraduate and lower-division graduate classes in statistics and data-intensive sciences. The aim is to provide students with the understanding of how data are generated and interpreted at the earliest stage of their statistics education. To that end, the book empowers students with models and tools that answer nontrivial causal questions using vivid examples and simple mathematics. Topics include: causal models, model testing, effects of interventions, mediation and counterfactuals, in both linear and nonparametric systems.

The Table of Contents, Preface and excerpts from the four chapters can be viewed here:
http://bayes.cs.ucla.edu/PRIMER/
A book website providing answers to home-works and interactive computer programs for simulation and analysis (using dagitty)  is currently under construction.

B1
We are in receipt of the fourth edition of Rex Kline’s book “Principles and Practice of Structural Equation Modeling”, http://psychology.concordia.ca/fac/kline/books/nta.pdf

This book is unique in that it treats structural equation models (SEMs) as carriers of causal assumptions and tools for causal inference. Gone are the inhibitions and trepidation that characterize most SEM texts in their treatments of causation.

To the best of my knowledge, Chapter 8 in Kline’s book is the first SEM text to introduce graphical criteria for parameter identification — a long overdue tool
in a field that depends on identifiability for model “fitting”. Overall, the book elevates SEM education to new heights and promises to usher a renaissance for a field that, five decades ago, has pioneered causal analysis in the behavioral sciences.

B2
Much has been written lately on computer ethics, morality, and free will. The new book “Programming Machine Ethics” by Luis Moniz Pereira and Ari Saptawijaya formalizes these concepts in the language of logic programming. See book announcement http://www.springer.com/gp/book/9783319293530. As a novice to the literature on ethics and morality, I was happy to find a comprehensive compilation of the many philosophical works on these topics, articulated in a language that even a layman can comprehend. I was also happy to see the critical role that the logic of counterfactuals plays in moral reasoning. The book is a refreshing reminder that there is more to counterfactual reasoning than “average treatment effects”.

C. News, Journals, awards and other frills.
C1.
Nominations are Invited for the Causality in Statistics Education Award (Deadline is February 15, 2016).

The ASA Causality in Statistics Education Award is aimed at encouraging the teaching of basic causal inference in introductory statistics courses. Co-sponsored by Microsoft Research and Google, the prize is motivated by the growing importance of introducing core elements of causal inference into undergraduate and lower-division graduate classes in statistics. For more information, please see http://www.amstat.org/education/causalityprize/ .

Nominations and questions should be sent to the ASA office at educinfo@amstat.org . The nomination deadline is February 15, 2016.

C.2.
Issue 4.1 of the Journal of Causal Inference is scheduled to appear March 2016, with articles covering all aspects of causal analysis. For mission, policy, and submission information please see: http://degruyter.com/view/j/jci

C.3
Finally, enjoy new results and new insights posted on our technical report page: http://bayes.cs.ucla.edu/csl_papers.html

Judea

UAB’s Nutrition Obesity Research Center — Causal Inference Course

Filed under: Announcement,Uncategorized — bryantc @ 1:03 am
We received the following announcement from Richard F. Sarver (UAB):

UAB’s Nutrition Obesity Research Center invite you to join them at one or both of our five-day short courses at the University of Alabama at Birmingham.

June: The Mathematical Sciences in Obesity Research The mathematical sciences including engineering, statistics, computer science, physics, econometrics, psychometrics, epidemiology, and mathematics qua mathematics are increasingly being applied to advance our understanding of the causes, consequences, and alleviation of obesity. These applications do not merely involve routine well-established approaches easily implemented in widely available commercial software. Rather, they increasingly involve computationally demanding tasks, use and in some cases development of novel analytic methods and software, new derivations, computer simulations, and unprecedented interdigitation of two or more existing techniques. Such advances at the interface of the mathematical sciences and obesity research require bilateral training and exposure for investigators in both disciplines. July: Strengthening Causal Inference in Behavioral Obesity Research Identifying causal relations among variables is fundamental to science. Obesity is a major problem for which much progress in understanding, treatment, and prevention remains to be made. Understanding which social and behavioral factors cause variations in adiposity and which other factors cause variations is vital to producing, evaluating, and selecting intervention and prevention strategies. In addition, developing a greater understanding of obesity’s causes, requires input from diverse disciplines including statistics, economics, psychology, epidemiology, mathematics, philosophy, and in some cases behavioral or statistical genetics. However, applying techniques from these disciplines does not involve routine well-known ‘cookbook’ approaches but requires an understanding of the underlying principles, so the investigator can tailor approaches to specific and varying situations. For full details of each of the courses, please refer to our websites below: Mon 6/13/2016 – Fri 6/17/2016: The Mathematical Sciences in Obesity, http://www.soph.uab.edu/energetics/shortcourse/third Mon 7/25/2016 – Fri 7/29/2016: Strengthening Causal Inference in Behavioral Obesity Research, http://www.soph.uab.edu/energetics/causal_inference_shortcourse/second Limited travel scholarships are available to young investigators. Please apply by Fri 4/1/2016 and be notified of acceptance by Fri 4/8/2016. Women, members of underrepresented minority groups and individuals with disabilities are strongly encouraged to apply. We look forward to seeing you in Birmingham this summer!

February 1, 2016

Workshop on Statistical Causal Inference and its Applications to Genetics

Filed under: Announcement — bryantc @ 11:50 pm

We received the following announcement from Robin Evans (University of Oxford):

Statistical Causal Inference and its Applications to Genetics, to be held at CRM in Montreal, July 2529 2016.

Additional information can be found here: http://www.crm.umontreal.ca/2016/Genetics16/index_e.php

Dear Colleagues,

We are very excited to announce a week long workshop in Statistical Causal Inference and its Applications to Genetics, to be held at CRM in Montreal, July 2529 2016.

We seek participants from Statistics and Biology to discuss the cutting-edge inferential causal problems in the discipline. Points for discussion will include

– modern datasets in genetics,
– methods to deal with huge quantities of data from multiple experimental settings,
– hypothesis generation from limited experimental data,
– efficient experimental design,
– incorporation of prior information in a computationally tractable way,
– causal methods for time series data,
– Mendelian randomization,

We strongly encourage the participation of junior researchers, and invite the submission of abstracts for oral and poster presentations. To register your interest in participating or presenting please visit our website.

Invited speakers include:

Elias Bareinboim (Purdue University)
Tom Claassen (Radboud University Nijmegen)
Denver Dash (University of Pittsburgh)
Philip Dawid (University of Cambridge)
Vanessa Didelez (University of Bristol)
Frederick Eberhardt (Caltech)
Michael Eichler (Maastricht University)
Julien Gagneur (LMU, Gene Center)
Celia Greenwood (Lady Davis Institute for Medical Research)
Niels Richard Hansen (University of Copenhagen)
Dominik Janzing (Max-Planck-Institute for Intelligent Systems)
Samantha Kleinberg (Stevens Institute of Technology)
Aurélie Labbe (McGill University)
Steffen Lauritzen (University of Oxford)
Po-Ling Loh (University of Pennsylvania)
Sisi Ma (New York University)
Daniel Marbach (Université de Lausanne)
John Marioni (EMBL-EBI)
Lawrence McCandless (Simon Fraser University)
Joris Mooij (AMLab, University of Amsterdam)
Dana Pe’er (Columbia University )
Jonas Peters (MPI for Intelligent Systems)
Garvesh Raskutti (University of Wisconsin-Madison)
Thomas S. Richardson (University of Washington)
James Robins (Harvard School of Public Health)
Olli Saarela (University of Toronto)
Karen Sachs (Stanford University)
Shohei Shimizu (Osaka University)
Ricardo Silva (UCL)
George Davey Smith (University of Bristol)
Peter Spirtes (Carnegie Mellon University)
Oliver Stegle (EMBL-EBI)
Simon Tavare (University of Cambridge )
Jin Tian (Iowa State University)
Achim Tresch (Max Planck Institute)
Ioannis Tsamardinos (ICS – FORTH)

Best regards,

The organisers:

Robin Evans, University of Oxford
Chris Holmes, University of Oxford
Marloes Maathuis, ETH Zurich
Erica Moodie, McGill
Ilya Shpitser, Johns Hopkins
David Stephens, McGill
Caroline Uhler, MIT

We’re very grateful to the workshop sponsors: CRM, CANSSI and PIMS.

November 1, 2015

System Reconfiguration

Filed under: Announcement — bryantc @ 1:38 am

Sorry there will be no entries this Fall due to system reconfiguration.  Please bear with us, we will be back in February 2016.

August 11, 2015

Mid-Summer Greeting from the UCLA Causality Blog

Filed under: Announcement,Causal Effect,Counterfactual,General — moderator @ 6:09 pm

Friends in causality research,

This mid-summer greeting of UCLA Causality blog contains:
A. News items concerning causality research
B. Discussions and scientific results

1. The next issue of the Journal of Causal Inference is scheduled to appear this month, and the table of content can be viewed here.

2. A new digital journal “Observational Studies” is out this month (link) and its first issue is dedicated to the legacy of William Cochran (1909-1980).

My contribution to this issue can be viewed here:
http://ftp.cs.ucla.edu/pub/stat_ser/r456.pdf

See also comment 1 below.

3. A video recording of my Cassel Lecture at the SER conference, June 2015, Denver, CO, can be viewed here:
https://epiresearch.org/about-us/archives/video-archives-2/the-scientific-approach-to-causal-inference/

4. A video of a conversation with Robert Gould concerning the teaching of causality can be viewed on Wiley’s Statistics Views, link (2 parts, scroll down).

5. We are informed of the upcoming publication of a new book, Rex Kline “Principles and Practice of Structural Equation Modeling, Fourth Edition (link). Judging by the chapters I read, this book promises to be unique; it treats structural equation models for what they are: carriers of causal assumptions and tools for causal inference. Kudos, Rex.

6. We are informed of another book on causal inference: Imbens, Guido W.; Rubin, Donald B. “Causal Inference in Statistics, Social, and Biomedical Sciences: An Introduction” Cambridge University Press (2015). Readers will quickly realize that the ideas, methods, and tools discussed on this blog were kept out of this book. Omissions include: Control of confounding, testable implications of causal assumptions, visualization of causal assumptions, generalized instrumental variables, mediation analysis, moderation, interaction, attribution, external validity, explanation, representation of scientific knowledge and, most importantly, the unification of potential outcomes and structural models.

Given that the book is advertised as describing “the leading analysis methods” of causal inference, unsuspecting readers will get the impression that the field as a whole is facing fundamental obstacles, and that we are still lacking the tools to cope with basic causal tasks such as confounding control and model testing. I do not believe mainstream methods of causal inference are in such state of helplessness.

The authors’ motivation and rationale for this exclusion were discussed at length on this blog. See
“Are economists smarter than epidemiologists”
http://causality.cs.ucla.edu/blog/?p=1241

and “On the First Law of Causal Inference”
http://causality.cs.ucla.edu/blog/?m=201411

As most of you know, I have spent many hours trying to explain to leaders of the potential outcome school what insights and tools their students would be missing if not given exposure to a broader intellectual environment, one that embraces model-based inferences side by side with potential outcomes.

This book confirms my concerns, and its insularity-based impediments are likely to evoke interesting public discussions on the subject. For example, educators will undoubtedly wish to ask:

(1) Is there any guidance we can give students on how to select covariates for matching or adjustment?.

(2) Are there any tools available to help students judge the plausibility of ignorability-type assumptions?

(3) Aren’t there any methods for deciding whether identifying assumptions have testable implications?.

I believe that if such questions are asked often enough, they will eventually evoke non-ignorable answers.

7. The ASA has come up with a press release yesterday, recognizing Tyler VanderWeele’s new book “Explanation in Causal Inference,” winner of the 2015 Causality in Statistics Education Award
http://www.amstat.org/newsroom/pressreleases/JSM2015-CausalityinStatisticsEducationAward.pdf

Congratulations, Tyler.

Information on nominations for the 2016 Award will soon be announced.

8. Since our last Greetings (Spring, 2015) we have had a few lively discussions posted on this blog. I summarize them below:

8.1. Indirect Confounding and Causal Calculus
(How getting too anxious to criticize do-calculus may cause you to miss an easy solution to a problem you thought was hard).
July 23, 2015
http://causality.cs.ucla.edu/blog/?p=1545

8.2. Does Obesity Shorten Life? Or is it the Soda?
(Discusses whether it was the earth that caused the apple to fall? or the gravitational field created by the earth?.)
May 27, 2015
http://causality.cs.ucla.edu/blog/?p=1534

8.3. Causation without Manipulation
(Asks whether anyone takes this mantra seriously nowadays, and whether we need manipulations to store scientific knowledge)
May 14, 2015
http://causality.cs.ucla.edu/blog/?p=1518

8.4. David Freedman, Statistics, and Structural Equation Models
(On why Freedman invented “response schedule”?)
May 6, 2015
http://causality.cs.ucla.edu/blog/?p=1502

8.5. We also had a few breakthroughs posted on our technical report page
http://bayes.cs.ucla.edu/csl_papers.html

My favorites this summer are these two:
http://ftp.cs.ucla.edu/pub/stat_ser/r452.pdf
http://ftp.cs.ucla.edu/pub/stat_ser/r450.pdf
because they deal with the tough and long-standing problem:
“How generalizable are empirical studies?”

Enjoy the rest of the summer
Judea

April 29, 2015

Spring Greeting from the UCLA Causality Blog

Filed under: Announcement,Causal Effect,Generalizability — eb @ 12:17 am

Friends in causality research,

This Spring greeting from UCLA Causality blog contains:
A. News items concerning causality research,
B. New postings, new problems and new solutions.

A. News items concerning causality research
A1. Congratulations go to Tyler VanderWeele, winner of the 2015 ASA “Causality in Statistics Education Award” for his book “Explanation in Causal Inference” (Oxford, 2015). Thanks, Tyler. The award ceremony will take place at the 2015 JSM conference, August 8-13, in Seattle.

Another good news, Google has joined Microsoft in sponsoring next year’s award, so please upgrade your 2016 nominations. For details of nominations and selection criteria, see http://www.amstat.org/education/causalityprize/

A2. Vol. 3 Issue 1 (March 2015) of the Journal of Causal Inference (JCI) is now in print.
The Table of Content and full text pdf can be viewed here. Submissions are welcome on all aspects of causal analysis. A highly urgent request is in place: Please start your article with a crisp description of the research problem addressed.

A3. 2015 Atlantic Causal Inference
The 2015 Atlantic Causal Conference will take place in Philadelphia, May 20th through May 21 2015. The web site for the registration and conference is http://www.med.upenn.edu/cceb/biostat/conferences/ACIC15/index_acic15.php

A4. A 2-Day Course: Causal Inference with Graphical Models will be offered in San Jose, CA, on June 15-16, by professor Felix Elwert (University of Wisconsin). The organizers (BayesiaLab) offer generous dacademic discounts to students and faculty. See here.

B. New postings, new problems and new solutions.

B1. Causality and Big data

The National Academy of Sciences has organized a colloquium on “Drawing Causal Inference from Big Data”. The colloquium took place March 26-27, in Washington DC, and reflected a growing realization that statistical analysis void of causal explanations would not satisfy users of big data systems. The colloquium program can be viewed here:
http://www.nasonline.org/programs/sackler-colloquia/completed_colloquia/Big-data.html

My talk (with E. Bareinboim) focused on the problem of fusing data from multiple sources so as to provide valid answers to causal questions of interest. The main point was that this seemingly hopeless task can now be reduced to mathematics. See abstract and slides here: http://www.nasonline.org/programs/sackler-colloquia/documents/pearl1.pdf
and a youtube video here: https://www.youtube.com/watch?v=sjtBalq7Ulc

B2. A recent post on our blog deals with one of the most crucial and puzzling questions of causal inference: “How generalizable are our randomized clinical trials?” It turns out that the tools developed for transportability theory in http://ftp.cs.ucla.edu/pub/stat_ser/r400.pdf also provide an elegant answer to this question. Our post compares this answer to the way researchers have attempted to tackle the problem using the language of ignorability, usually resorting to post-stratification. It turns out that ignorability-type assumptions are fairly limited, both in their ability to define conditions that permit generalizations, and in the way they impede interpretation in specific applications.

B3. We welcome the journal publication of the following research reports, Please update your citations:

B3.1 On the interpretation and Identification of mediation
Link: http://ftp.cs.ucla.edu/pub/stat_ser/r389.pdf

B3.2 On transportability
Link: http://ftp.cs.ucla.edu/pub/stat_ser/r400.pdf

B3.3 Back to mediation
Link: http://ftp.cs.ucla.edu/pub/stat_ser/r421-reprint.pdf

B4. Finally, enjoy our recent fruits on
http://bayes.cs.ucla.edu/csl_papers.html

Cheers,
Judea

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