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+# `A resource list for causality in statistics, data science and physics.`
+
+## Books
+
+Reverse chronological order, both technical and popular.
+
+* Pearl and Mackenzie
+  The Book of Why: The New Science of Cause and Effect (2018)
+  [amzn](https://www.amzn.com/dp/046509760X)
+
+* Hernán & Robins
+  Causal Inference (2018)
+  [online](http://bit.ly/2mSeeXI)
+
+* Rosenbaum
+  Observation and Experiment: An Introduction to Causal Inference (2017)
+  [amzn](https://www.amzn.com/dp/067497557X/)
+
+* Jonas Peters, Dominik Janzing and Bernhard Schoelkopf
+  Elements of Causal Ingerence: Foundations and Learning Algorithms (2017)
+  [mitpress](https://mitpress.mit.edu/books/elements-causal-inference)
+
+* Pearl, Glymour and Jewell,
+  Causal Inference in Statistics: A Primer (2016)
+  [amzn](https://www.amzn.com/dp/1119186846)
+
+* Morgan & Winship,
+  Counterfactuals and Causal Inference (2nd edition) (2015)
+  [amzn](https://www.amzn.com/dp/1107694167)
+
+* Causal Inference for Statistics, Social, and Biomedical Sciences: 
+  An Introduction, Imbens & Rubin, (2015)
+  [amzn](https://www.amzn.com/dp/0521885884/)
+
+* Angrist & Pischke 
+  Mostly Harmless Econometrics (2009)
+  [amzn](https://www.amzn.com/dp/0691120358/)
+  [princeton](https://press.princeton.edu/titles/8769.html)
+
+* Judea Perl 
+  Causality: Models, Reasoning and Inference (2009) 2nd Edition
+  [amzn](https://www.amz.com/dp/052189560X)
+
+* Econometric Causality, 
+  James J. Heckman
+  International Statistical Review (2008), 76, 1, 1–27 
+  [doi](http://dx.doi.org/10.1111/j.1751-5823.2007.00024.x)
+
+* Rosenbaum  
+  Observational Studies (Springer Series in Statistics) 2nd Edition  (2002)
+  [amzn](https://www.amzn.com/dp/0387989676)
+
+
+
+## Papers
+
+* Theoretical Impediments to Machine Learning With Seven Sparks 
+  from the Causal Revolution, Judea Pearl
+  [arXiv:1801.04016](https://arxiv.org/abs/1801.04016)
+
+* Brodersen KH, Gallusser F, Koehler J, Remy N, Scott SL. 
+  Inferring causal impact using Bayesian structural time-series models. 
+  Annals of Applied Statistics, (2015), Vol. 9, No. 1, 247-274. 
+  [link](http://research.google.com/pubs/pub41854.html)
+
+* Introduction to Causal Inference
+  Peter Spirtes
+  (2010) [jmlr](http://www.jmlr.org/papers/v11/spirtes10a.html)
+
+* Causal inference in statistics:An overview 
+  Judea Pearl. (2009) [doi](http://dx.doi.org/10.1214/09-SS057)
+
+* Rubin, D. B. (1974). Estimating causal effects of treatments 
+  in randomized and nonrandomized studies. 
+  Journal of Educational Psychology, 66(5), 688-701.
+  [doi](http://dx.doi.org/10.1037/h0037350)
+
+* Haavelmo, T. (1943). 
+  The statistical implications of a system of simultaneous equations. 
+  Econometrica, 11, 1–12.
+  [jstor](http://links.jstor.org/sici?sici=0012-9682%28194301%2911%3A1%3C1%3ATSIOAS%3E2.0.CO%3B2-N)
+
+## Software
+
+* dagR: Directed Acyclic Graph with R
+  [CRAN](https://cran.r-project.org/web/packages/dagR/index.html)[doi](http://dx.doi.org/10.1097/EDE.0b013e3181e09112)
+
+* An R package for causal inference using Bayesian structural 
+  time-series models 
+  [CausalImpact](https://google.github.io/CausalImpact/CausalImpact.html)
+  [CRAN](https://cran.r-project.org/package=CausalImpact)
+
+* Python package [causalinference](https://github.com/laurencium/causalinference) [Vignette](https://github.com/laurencium/causalinference/blob/master/docs/tex/vignette.pdf)
+
+* Tetrad Project: Graphical Causal Models [homepage](http://www.phil.cmu.edu/tetrad/)
+
+* PyPhi: A toolbox for integrated information theory [arXiv](https://arxiv.org/abs/1712.09644)
+
+## MOOCs
+
+* A Crash Course in Causality: Inferring Causal Effects from Observational Data [link](https://www.coursera.org/learn/crash-course-in-causality)
+
+* Measuring Causal Effects in the Social Sciences [link](https://www.coursera.org/learn/causal-effects)
+
+## Quotes: Prediction and Causation
+
+* " Actually correlation lets you make predictions 
+   in many cases, assuming you're making prediction 
+   about the world as reflected in your data. 
+   For example, correlations between photos and 
+   their labels allows you usually to make predictions 
+   about new photos. 
+
+   The problem gets difficult if you want to predict the 
+   effects of actions taken in a different manner from 
+   that which exists in your data. For example, if you 
+   want to consider different treatment strategies for 
+   cancer using data from past cancer patients, only 
+   correlations will usually not suffice as you're 
+   trying to predict counterfactual that might not exist in your data."
+   Uri Shalit, Technion (Forum Communication 01/2018)
+
+*  Reply to Uri: 
+
+    "You don't have to have correlation to make a prediction in any case.  
+    Chicken entrails used to suffice, and still do some places.  
+    I would argue that chicken entrails might actually be better 
+    than relying purely upon correlation if you know nothing 
+    about any causality involved.
+
+     Only if the correlation is the result of causality will 
+     you be able to trust a prediction using correlation.  
+     This is where the "science" in "data science" usually disappears, 
+     as exemplified in your post."
+
+     Recall, Fisher himself (while employed by the tobacco industry) 
+     claimed that any link between lung cancer and smoking was mistaking 
+     correlation for causality.  Of course, Fisher, a life long smoker, 
+     died from lung cancer also.  Talk about causality bites, 
+     predicting Fisher's means of death via the correlation would 
+     have been trustable."
+     Mark Powell, Austin (Forum Communication 01/2018)
+
+## Quotes: Rubin vs. Pearl
+
+* " Rubin and Pearl are kind of "academic enemies". 
+  Though neither completely dismisses the other, 
+  they both make snide remarks about the other's work. 
+  Pearl shows in his book exactly how Neyman-Rubin 
+  potential outcomes can be derived from causal graphs. 
+  As far as I know Rubin never really makes an 
+  attempt to address Pearl's ideas directly. 
+  However, Rubin, being a statistician, made 
+  significant contributions to the practice of real-world 
+  causal inference, which go beyond Pearl's interests. 
+  Jamie Robins also made seminal contributions to this subject.
+  You can read some of the debate on Andrew Gelman's blog 
+  [here](http://andrewgelman.com/2009/07/05/disputes_about/)
+  Pearl writes in the comment section and in that blog  
+  post there are links to follow up posts. "
+  Uri Shalit, Technion (Forum Communication 01/2018)
+
+## Quotes: On the Pearl's Philosopy
+
+* "..while Pearl's work is foundational, and its importance  
+  cannot be overstated, his published work is often 
+  insufficient in addressing the real-world problems of 
+  many data scientists. The reason is that Pearl is mostly 
+  concerned with the problem of identification, i.e. which data 
+  generating processes allow us to infer causation from observed data. 
+  He is less concerned with the statistical problem of actually 
+  inferring these purported causal relationships from data. 
+  This is especially true if the data is high-dimensional 
+   or noisy (Pearl usually considers a few binary or Gaussian variables)."
+  Uri Shalit, Technion (Forum Communication 01/2018)