(*Notes on using UW's proxy server to access some of these papers.*)

Morgan and Winship: Section 1.1; Sections 2.1-2.5;

Angrist and Pischke: Chapter 1, and Chapter 2.1-2.2

Samuel Pepys, Isaac Newton and Probability
E. Rubin and E.D. Schell. * The American Statistician * Vol.14, No.4, 27-30.

Statistics and Causal Inference Paul W. Holland
* Journal of the American Statistical Association *, Vol. 81, No. 396. (Dec., 1986), pp. 945-960.

Estimating causal effects of treatments in randomized and nonrandomized studies
Don B. Rubin * Journal of Educational Psychology*, Vol 66(5), Oct, 1974. pp. 688-701

On the Application of Probability Theory to Agricultural Experiments J.Neyman (D.M.Dabrowska, T.P.Speed, Trans.). * Statistical Science * Vol.5, No.4, 465-472.

The Consistency Statement in Causal Inference:
A Definition or an Assumption? S.R. Cole and C.E. Frangakis
* Epidemiology *, Vol. 20, No. 1. (Jan., 2009), pp. 3-5.

Concerning the Consistency Assumption in Causal Inference
T.J. VanderWeele
* Epidemiology *, Vol. 20, No. 6. (Nov., 2009), pp. 880-883.

On the Consistency Rule in Causal Inference: Axiom, Definition, Assumption, or Theorem?
J. Pearl
* Epidemiology *, Vol. 21, No. 6. (Nov., 2010), pp. 872-875.

A Potential Tale of Two by Two Tables from Completely Randomized Experiments
Peng Ding and Tirthankar Dasgupta (2015) * Journal of the American Statistical Association*, In press.

Randomization inference for treatment effects on a binary outcome Joseph Rigdon and Michael Hudgens (2015) * Statistics in Medicine *, Vol. 34, pp. 924-935.

Effects Attributable to Treatment: Inference in Experiments and Observational Studies with a Discrete Pivot Paul Rosenbaum (2001)
* Biometrika *, Vol. 88, No. 1., pp. 219-231.

Exact confidence intervals for the average causal effect on a binary outcome
Xinran Li and Peng Ding (2015) * Statistics in Medicine *, In press.

Sharp bounds on the variance in randomized experiments
Peter Aronow, Donald P. Green, and Donald K. K. Lee (2014) * Annals of Statistics *, Volume 42, Number 3 (2014), 850-871. ArXiv version

Morgan and Winship:

Data, design, and background knowledge in etiologic inference. J.M. Robins (2001). Epidemiology, 11(3):313-320.

**Note:** * In this paper Robins uses a graphical rule that is
equivalent to d-connection,
but formulated differently (called 'moralization').
However, you should still be able to follow the examples by applying d-separation. *

Causal diagrams for epidemiologic research. Greenland S, Pearl J, Robins JM. (1999). Epidemiology, 10(1):37-48.

Identification of Causal Effects Using Instrumental Variables J.Angrist, G.W. Imbens and D.B. Rubin (1996), * Journal of the American Statistical Association *, Vol.91, pp.444-455.

Review article on
Mendelian Randomization as an instrumental variable approach to causal inference
by V. Didelez and N. Sheehan, * Stat Methods Med Res* 2007; 16; 309

A clinician's tool for analyzing non-compliance article by Max Chickering and Judea Pearl, published in AAAI 1996.

Assessing the effect of an influenza vaccine in an
encouragement design
K. Hirano, G.W. Imbens, D.B. Rubin and X.-H. Zhou, * Biostatistics * (2000), Vol.1, pp.69--88.

Transparent Parametrizations of Models for Potential Outcomes
T.S. Richardson, R.J. Evans and J.M. Robins
* Proceedings of Valencia IX *, In press.

Analysis of the Binary Instrumental Variable Model . T.S. Richardson and J.M. Robins (2010). In * Heuristics, Probability and Causality: A Tribute to Judea Pearl * H. Geffner and J.Y. Halpern, Editors, College Publications, UK.

Against Odds Ratios article by Altman , Deeks, and Sackett . Note also the two minor corrections (1) , (2) .