The acquisition of legal causal and predictive knowledge is discussed under empirical-statistical acquisition methods and deductive rational methods. Empirical-statistical analysis involves obtaining information on persons, places, things, occurrences, entities, units of analysis, and other objects of study. Deductive-rational analysis begins with one or more premises established through empirical-statistical analysis, prior deduction, or intuitive acceptance. From these premises, the researcher draws causal relations without measuring predictor and predicted variables. The discussion of causal analysis through empirical-statistical methods focuses on causal analysis and the legal process and causal relations between goals and policies in time-series analysis. The section on causal analysis through deductive-rational methods covers deductive modeling in policy analysis and identification of the impact of legal policy changes before they occur. Causal analysis applications encompass the effects of reducing judicial sentencing discretion, the multiple correlation of judicial backgrounds and decisions, and the impact of jury size on convicting probability. Topics pertaining to the application of predictive analysis include the judicial prediction service, case outcome prediction by 'staircase' tables and percentaging, and the use of microcomputers to predict case outcome. 200 references and name and subject indexes.