Anchoring-Based Causal Design (ABCD): Estimating the Effects of Beliefs

Abstract

A central challenge in any study of the effects of beliefs on outcomes, such as decisions and behavior, is the risk of omitted variables bias. Omitted variables, frequently unmeasured or even unknown, can induce correlations between beliefs and decisions that are not genuinely causal, in which case the omitted variables are referred to as confounders. To address the challenge of causal inference, researchers frequently rely on information provision experiments to randomly manipulate beliefs. The information supplied in these experiments can serve as an instrumental variable (IV), enabling causal inference, so long as it influences decisions exclusively through its impact on beliefs. However, providing varying information to participants to shape their beliefs can raise both methodological and ethical concerns. Methodological concerns arise from potential violations of the exclusion restriction assumption. Such violations may stem from information source effects, when attitudes toward the source affect the outcome decision directly, thereby introducing a confounder. An ethical concern arises from manipulating the provided information, as it may involve deceiving participants. This paper proposes and empirically demonstrates a new method for treating beliefs and estimating their effects, the Anchoring-Based Causal Design (ABCD), which avoids deception and source influences. ABCD combines the cognitive mechanism known as anchoring with instrumental variable (IV) estimation. Instead of providing substantive information, the method employs a deliberately non-informative procedure in which participants compare their self-assessment of a concept to a randomly assigned anchor value. We present the method and the results of eight experiments demonstrating its application, strengths, and limitations. We conclude by discussing the potential of this design for advancing experimental social science.