An Assessment of Causal Inference in Smoking Initiation Research and a Framework for Future Research

By Heckman, James J.; Flyer, Fredrick et al. | Economic Inquiry, January 2008 | Go to article overview
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An Assessment of Causal Inference in Smoking Initiation Research and a Framework for Future Research


Heckman, James J., Flyer, Fredrick, Loughlin, Colleen, Economic Inquiry


I. INTRODUCTION

The causal factors underlying youth smoking initiation are central to the challenge of developing effective smoking prevention programs and the shaping of many other types of smoking-related policies. These causal factors, specifically the ones that potentially may be influenced by the conduct of cigarette manufacturers, also have been a central issue in much of the recent smoking-related litigation. These high-profile legal cases have focused on the claim that tobacco company conduct significantly influenced youth smoking initiation rates. In particular, cigarette advertising and tobacco company public reluctance to acknowledge the health risks of smoking have been purported to be key factors influencing youth smoking decisions. (1)

In this paper, we examine the basic principles of empirical scientific investigation that have been adopted to establish causal relationships and apply these principles to evaluate the evidence underlying the link between cigarette advertising and youth smoking initiation. The causal link between tobacco company marketing and youth smoking generally has been accepted as empirically established outside the economics literature, despite the fact that the underlying evidence fails to meet commonly applied econometric standards. We review some of these papers in the public health literature that have been cited as evidence supporting the causal link between cigarette advertising and youth smoking and explain why these papers fail to support this causal link. Further, we also review the economics literature that provides evidence for a multicausal model for youth risky behaviors, including smoking. We identify studies in which researchers have found that many characteristics affecting choices to participate in risky behaviors are formed at very early ages.

Empirical demonstration of causation is fundamental to guiding effective development of interventions in many areas of social life. Scientific principles are especially important for developing smoking-related policies, as enormous resources currently are being devoted to develop programs that attempt to reduce youth smoking propensities. However, many of these policies (and suggested policies) focus on causal factors that have not been scientifically established but rather merely assumed to affect smoking initiation. More effective policies potentially could be developed by distinguishing causal factors through more reliable empirical methodologies. Without identifying factors that lead to smoking initiation through sound estimation techniques, current prevention efforts may turn out to have little or muted benefits. (2)

II. CAUSAL FACTORS OF SMOKING INITIATION AND THE SCIENTIFIC MODEL

An important goal of policy analysis is to identify and measure causal relationships, as many confounding correlations exist which do not reflect structural relations underlying actual outcomes. Understanding these underlying structural relations allows empirical versions of estimated models to more accurately forecast the effects of interventions. As is well recognized in the econometrics literature, precise identification and measurement of causal effects is a difficult task in the area of human choice, since input parameters of investigated relationships may reflect unobserved individual characteristics and choices. Thus, correlations between endogenous input parameters and observed outcomes may simply represent individual sorting on latent characteristics, rather than structural relations between input and output variables.

Many times in other disciplines, biases associated with endogeneity do not hinder estimation of structural models because observed variables are not affected by individual choice. For example, to evaluate the effect of a particular drug on a particular disease, researchers may be able to perfectly control factors such as the presence of other diseases, dosage regimens, alternative drug usage, and diet.

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