CLARK GLYMOUR, PETER SPIRTES AND RICHARD SCHEINES IN PLACE OF REGRESSION ABSTRACT. Assuming an adaptation of Suppes's analysis of causality, we show that multiple regression methods are fundamentally incorrect procedures for identifying causes. This is because when regressors are correlated the existence of an unmeasured common cause of regressor Xi and outcome variable Y may bias estimates of the influence of other regressors Xk; variables having no influence on Y whatsoever may thereby be given significant regression coefficients. The bias may be quite ·large. Simulation studies show that standard regression model specification procedures make the same error. The strategy of regressing on a larger set of variables and checking stability may compound rather than remedy the problem. A similar difficulty in the estimation of the influence of other regressors arises if some Xi is an effect rather than a cause ofY. The problem appears endemic in uses of multiple regression on uncontrolled variables, and unless somehow corrected appears to invalidate many scientific uses of regression methods. We describe an implementation in the TETRAD II program of a model specification algorithm that avoids these and certain other errors in large samples. We illustrate the TETRAD II algorithm by applying it to a number of real and simulated data sets. The social sciences often use non-experimental or quasi-experimental data, and multiple regression is the principal tool for causal inference in such settings. Multiple regression, whether linear or non-linear, is the preeminent statistical device through which hypotheses are confirmed, conjectures formed, and policies suggested or justified. Whether you read about education, human fertility and population growth, the epi- demiology of pollutants, or almost any other topic of urgent human concern, you will find data analyzed by regression methods to identify which variables inflence an outcome variable of interest, and to esti- mate the strength of those influences. But multiple regression, whether linear and non-linear and the variety of search procedures that have developed around it, are fundamentally incorrect methods for the first purpose, and the results are demonstrably unreliable. The deepest prob- lem with regression is that it mistakes the connection between causation and probability; that error cannot be corrected by increased sample sizes, or by testing for linearity or autocorrelation, or by transforming 339 P. Humphreys (ed.), Patrick Suppes: Scientific Philosopher, Vol. 1, 339-365. © 1994 Kluwer Academic Publishers.