EI.SEVIER EDITORIAL Can Econometrics Rescue Epidemiology? Has the science of epidemiology reached its limits? Ac- cording to a recent report in Science, epidemiology is losing credibility and causing unjustified fears because it is trying to find subtle links between disease, diet, lifestyle, and envi- ronmental factors (1). Reports by the popular press about epidernlolilgical studies are accused of contributing to un- necessary alarms and public misunderstanding (2). Econometric methods may be helpful to epidemiologists in rheir search for subtle links. Some of the problems in epidemic&@al studies may be detected by econometric approaches. In this issue of the AnnaEs of Epidemiology, Namvar Zohor)ri and David Savitz discuss the econometric conceprs oiendogeneity and unobserved heterogeneity (E&C UH) (3). A related paper by Zohoori shows how breast- feeding may be used by some women to delay their return to fertility and how their actions can hide the exact biological relation between breast-feeding and the return of menses (4). The key point is that a simple regression analysis may mislead if ir does not recognize chat one of its independent variables may be correlated with the error term because of intervening human action. The auci~~s explain how to use “instrumental” variables to tix these problems. Their explanation is nothing new. As explained below, an instrumental variable is essentially a substitute variable rhat is constructed to be free of the correlation with the error term that mars the original inde- pendent variable. Economists have been using instrumental variables for dec.ades to analyze everything from monetary policy to minimum wage laws (5-6). However, applications in ep&micrlogy have been rare, and they usually appear in journals of eccjnometrics and health economics. An excel- lent illustration is a recent paper by Shmueli (7) that has considered how an unobserved variable-here health status when an inditridual stopped smoking-may mask the rela- tionship hctween health status and the decision to stop smoking. J0ne.s (X), in a reply to Shmueli, used instrumental \:ariahles and ;in expanded data set ro show how the decision to srcip smoking may be influenced by either a desire to preserve current ~~xKI health or the physical limitation im- posed hy bad h&h. Karl Popper has had an important inAucncc (in the phi- losophy of science with his injunction that scientific hypoth- eses are useful only if they are falsitiablc with empirical evidence (9, 10). Zohoori and Savitz (3) hypothesize the existence ofE&UH. They show how instrumental variables can give different results from a simple regression if the hypothesized problem is present. They use the Hausman test to determine whether the simple and instrumental variable models are different enough to show the presence of the suspected problem. If rhe problem is presenr, rhe search for why the models differ can then he startell. Economists themselves are not completely en&us&tic, about instrumental variable estimators. A key step in devel- oping the estimator is to regress variable s (the variable suspected of being correlated with the error term) on a set of exogenous variables supposed to be highly correlated with x but uncorrelated with the error term cjt the original regression. The predicted values (i) of this “reduce&form” regression are then used as the “instrument~~l” variable in the original regression. Finding an appropriate XP of exogenous variables is sometimes problematic. Assuring th.at t-he rc- duced-form regression is not itself subject rcr ali that can go wrong in fitting a regression is still anothei- prttblem. Despite these technical difficulties, it I\ -not surprising that economerric tools are becoming useitul IO epidetniolo- gists. Hayek says that the distinguishing feat~ure of et:on<)mics is that it deals with “essentially complex phc’nomena” ( 11). Human action in a lnarketplace gives the c:conr)tnist little room for cc~ntrolling sources of variation in thcb zlasblc man- ner of biostatistics. Epidemiologists are &inning tll f&c the same methodologic problems as they MI: tt) account fol the interactions of biological laws and huulan hchavior in seuings outside nicely controlled studies. Where the two disciplines differ is th,:lr tc~~~nics has a theory thar specifies either the sign or magnitude ofcertain coeficients. For example, the quantity det~~and is supposed to Call as price rises; the total product c~f I&.~r is supposed to rise at a diminishing rate when other 6~ r( >rs &prt>ductiol~ are fixed. Because theory restricts the v&.:e\ OI ~OII~C cr>efhd cienrs, economic models can be fir more ~Xs111; tc bdiscover the exact \.aIues ofother cvefficienrs (e.g., rhc price elasticitv of dental ser\rices) about which only ;i range ~-au be specified. ln fact, sc~me economists question if it is :ruly “sclcntific” to include in a statistic;\1 model anv varishlths (ti.g~$ KICC or