Causal Inference in Civil Rights Litigation

Article excerpt

 
TABLE OF CONTENTS 
 
I. REGRESSION'S DIFFICULTIES AND THE ABSENCE OF A CAUSAL INFERENCE 
     FRAMEWORK                                                      540 
 
   A. What Is Regression?                                           540 
 
   B. Problems with Regression: Bias, Ill-Posed Questions, and 
        Specification Issues                                        543 
 
      1. Bias of the Analyst                                        544 
 
      2. Ill-Posed Questions                                        544 
 
      3. Nature of the Model                                        545 
 
      4. One Regression, or Two?                                    555 
 
   C. The Fundamental Problem: Absence of a Causal Framework        556 
 
II. POTENTIAL OUTCOMES: DEFINING CAUSAL EFFECTS AND BEYOND          557 
 
   A. Primitive Concepts: Treatment, Units, and the Fundamental 
        Problem                                                     558 
 
   B. Additional Units and the Non-Interference Assumption          560 
 
   C. Donating Values: Filling in the Missing Counterfactuals       562 
 
   D. Randomization: Balance in Background Variables                563 
 
   E. Observational Studies: Challenges and Some Ways 
        To Address Them                                             565 
 
   F. The Need for Lots of Background Variables                     573 
 
III. POTENTIAL OUTCOMES IN THE CIVIL RIGHTS LITIGATION SETTING      575 
 
   A. In General                                                    576 
 
      1. Identifying Primitive Concepts.                            576 
 
      2. A Tug-of-War: The Need for Background Variables Versus 
           the Desire To Detect Discrimination.                     581 
 
   B. Specific Contexts                                             583 
 
      1. Capital Punishment.                                        583 
 
      2. Employment Discrimination.                                 588 
 
      3. Causation and Section 2 of the Voting Rights Act           590 
 
IV. CONCLUSION                                                      597 
 
   "If they can get you asking the wrong questions, they don't have to 
   worry about answers." 
 
   -- Thomas Pynchon (1) 

In modern litigation, courts, attorneys, and expert witnesses use statistics in the hope of shedding light on questions of causation. This is particularly true in the civil rights context, where repetition of similar events makes the use of data analysis techniques attractive. The dialogue between law and quantitative methods in the civil rights area has lasted for decades, but few would characterize the relationship as happy. The disquiet is evident on both sides. As early as 1980, legal commentators concluded that many courts disregarded a substantial portion of statistical analyses they encountered in the employment discrimination context. (2) I Twenty-five years later, a survey of Title VII (3) cases demonstrated that little has changed. (4)

On the quantitative end, innovation has stagnated. During the decade or so that the Supreme Court was placing its imprimatur on statistics in general and regression in particular as appropriate forms of evidence in Title VII cases, (5) the academy was responding with scholarly examinations of quantitative issues arising in employment discrimination, (6) capital punishment, (7) redistricting, (8) and other contexts. (9) Quantitative analysts were convening panels and holding symposia to make recommendations to improve judicial understanding and use of statistical methods in litigation. (10) Those recommendations were ignored, (11) however, and a perusal of the hornbooks and looseleafs discussing the use of statistics as evidence in civil rights litigation suggests that the field is fixated on methods introduced decades ago, particularly regression, (12) despite judicial dissatisfaction. …