___

• Anderson, T.W. (1958). An Introduction to Multivariate Statistical Analysis, First Edition. New York: John Wiley and Sons.
• Berk, R.A. (2005). Randomized Experiments as the Bronze Standard. Journal of Experimental Criminology, 1(4): 417-433.
• Berk, R.A. (2008). Statistical Learning from a Regression Perspective, New York: Springer.
• Bishop, C.M. (2006). Pattern Recognition and Machine Learning. New York: Springer.
• Brown, L.D., M.L. Eaton, D.A. Freedman, S.P. Klein, R.A. Olshen, K.W. Wachter, M.T. Wells and D. Ylvisaker (1999). Statistical Controversies in Census 2000. Jurimetrics, 39: 347-375.
• Boullicaut, J-F., and C. Masson (2005). “Data Mining Query Languages”, (in: O. Maimon and L. Rokach -Ed. The Data Mining and Knowledge Discovery Handbook), New York: Springer.
• Cleveland, W.E. (1993). Visualizing Data. Summit, Jew Sersey: Hobart Press.
• Cook, D., Swayne, D.F., Buja, A., and T. Lang (2007). Interactive Dynamc Graphics and Data Analysis. New York: Springer.
• Dasu, T., and T, Johnson (2003). Exploratory Data Mining and Data Cleaning. New York: John Wiley and Sons.
• Diaconis, P. (1985). “Theories of Data Analysis: From Magical Thinking through Classical Statistics”. (in: D.C. Hoaglin, F. Mosteller, and J. Tukey -Ed., Exploring Data Tables, Trends, and Shapes), New York: John Wiley and Sons.
• Freedman, D.A. (2004). On Specifying Graphical Models for Causation, and the Identification Problem. Evaluation Review, 26: 267-293.
• Freedman, D.A. (2005). Statistical Models Theory and Practice. Cambrige University Press.
• Freedman, D.A. (2006). Statistical Models for Causation: What Inferential Leverage Do They Provide? Evaluation Review, 30: 691-713.
• Freedman, D.A. (2008a). Randomization Does not Justify Logistic Regression. Statistical Science, 23: 237-249.
• Freedman, D.A. (2008b). On Regression Adjustments to Experimental Data. Advances in Applied Mathematics, 40: 180-193.
• Freedman, D.A. and R.A. Berk (2008). On Weighting Regressions by Propensity Scores. Evaluation Review, 32: 392-409.
• Freedman, D.A. and P. Humphreys (1999). Are There Algorithms That Discover Causal Structure? Synthese, 121: 29-54.
• Freedman, D.A., P.B. Stark and K.W. Wachter (2001). A Probability Model for Census Adjustment. Mathematical Population Studies, 9: 165-180.
• Freedman, D.A. and K.W. Wachter (2001). Census Adjustment: Statistical Promise or Statistical Illusion? Society, 39: 26-33.
• Freedman, D.A. and K.W. Wachter (2003). “On The Likelihood of Improving the Accuracy of The Census Through Statistical Adjustment”. (in D. R. Goldstein -Ed., Science and Statistics: A Festschrift for Terry Speed. Institute of Mathematical Statistics Monograph 40: 197-230.)
• Gifi, A. (1990). Nonlinear Multivariate Analysis, New York: John Wiley and Sons.
• Hastie, T., R. Tibshirani and J. Friedman (2009). The Elements of Statistical Learning, Second Edition. New York: Springer.
• Heckman, J. (2000). Causal Parameters and Policy Analysis in Economics: A Twentieth Century Retrospective. Quarterly Journal of Economics, 88 (2): 47-97.
• Hoaglin, D.C., F. Mostellor and J. Tukey (2006), Understanding Robust and Exploratory Data Analysis, Wiley Classics Library. New York: John Wiley and Sons.
• Hoaglin, D.C., F. Mostellor and J. Tukey (2000). Exploring Data Tables, Trends, and Shapes, Wiley Classics Library. New York: John Wiley and Sons.
• Höppner, F. (2005). “Association Rules”, (in O. Maimon and L:. Rokach -Ed., The Data Mining and Knowledge Discovery Handbook), New York: Springer.
• Imbens, G. (2004). Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review. The Review of Economics and Statistics, 86 (1): 4-29.
• Imbens, G. and T. Lemieux (2008). Regression Discontinuity Designs: A Guide to Practice. Journal of Econometrics, 142: 611-614.
• King, R.D., J. Rowland, S.G. Oliver, M. Young, W. Aubrey, E. Byrne, M. Liakata, M. Markham, P. Pir, L.N. Soldatova, A. Sparkes, K.K. Whelan and A. Clare (2009). The Automation of Science. Science, 324 (3): 85-89.
• Leeb, H. and B.M. Pötscher (2005). Model Selection and Inference: Facts and Fiction, Econometric Theory, 21: 21–59.
• Leeb, H. and B.M. Pötscher (2006). Can one Estimate the Conditional Distribution of Post- Model-Selection Estimators? The Annals of Statistics, 34 (5): 2554–2591.
• Leeb, H. and B.M. Pötscher (2008). “Model Selection”, (in T.G. Anderson, R.A. Davis, J.P. Kreib, and T. Mikosch -Ed., The Handbook of Financial Time Series), New York, Springer: 785–821.
• Maimon, O., and L. Rokach (2005). The Data Mining and Knowledge Discovery Handbook. New York: Springer.
• Rubin, D.B. (2008). For Objective Causal Inference, Design Trumps Analysis. Annals of Applied Statistics, 2 (1): 808-840.
• Schmidt, M., and H. Lipson (2009). Distilling Free-Form Natural Laws from Exeprimental Data. Science, 324 (8): 81-85.
• Tukey, J. (1977). Exploratory Data Analysis. Reading, MA: Addison -Wesley.
• Wachter, K.W. and D.A. Freedman (2000). The Fifth Cell: Correlation Bias in U.S. Census Adjustment. Evaluation Review, 24: 191-211.
• Wachter, K.W. and D.A. Freedman (2000). Measuring Local Heterogeneity with 1990 U.S. Census Data. Demographic Research, 3 (10): 1-22.
• Wachter, K.W. and D.A. Freedman (1996). Planning for the Census in the Year 2000. Evaluation Review, 20: 355-377.