Overidentification is a term used to describe an overestimation of the causal effects of a given factor on an outcome. It occurs when a researcher uses a single study or analysis to draw numerous conclusions about the effects of a single factor on an outcome, without accounting for other factors that may be influencing the outcome. This can lead to inaccurate conclusions about the effectiveness of a given intervention or policy. In the social sciences, overidentification is an important issue to consider when conducting research and making policy decisions.
The concept of overidentification was first introduced by Nobel laureate economist Kenneth Arrow in a paper published in 1953. In this paper, Arrow argued that when multiple sources of evidence are used to measure a single phenomenon, the results may be overstated due to the presence of other unknown or unaccounted for factors. As such, he argued for the use of multiple sources of evidence to draw conclusions about the effects of a single factor on an outcome.
In the social sciences, overidentification is a common issue that researchers face when attempting to draw causal inferences from observational data. For example, when attempting to assess the impact of a given intervention on an outcome, a single study or analysis may not be able to account for all the other factors that are influencing the outcome. As such, researchers can be left with an over-simplified view of the effects of the intervention.
Overidentification can also occur when using experimental data to draw conclusions about causal relationships. This is because experiments are typically conducted in controlled settings, where other factors that may be influencing the outcome are held constant and may not be taken into account. As such, conclusions about the effects of a given factor on an outcome may be overstated.
Overidentification can also occur when using machine learning algorithms to draw causal inferences from observational data. Due to the complexity of the algorithms used in machine learning, it is difficult to identify all the factors that may be influencing the outcome. As such, the results of the algorithm can be overstated.
To avoid overidentification, researchers should use multiple sources of evidence to draw conclusions about the effects of a given factor on an outcome. This can include both observational and experimental data, as well as machine learning algorithms. Additionally, researchers should ensure that all potential factors that may be influencing the outcome are taken into account when drawing conclusions.
In conclusion, overidentification is an important issue to consider when conducting research and making policy decisions in the social sciences. It can occur when a single study or analysis is used to draw conclusions about the effects of a single factor on an outcome, without accounting for other factors that may be influencing the outcome. To avoid overidentification, researchers should use multiple sources of evidence and ensure that all potential factors that may be influencing the outcome are taken into account.
References
Arrow, K. (1953). Social Choice and Individual Values. New Haven: Yale University Press.
Hausman, J.A., & Wise, D.A. (1978). Contingent Valuation: Is Some Number Better than No Number? The Journal of Economic Perspectives, 2(4), 3-17.
Manski, C.F. (2013). Identification for Prediction and Decision. Cambridge: Harvard University Press.
Rubin, D.B. (1974). Estimating Causal Effects of Treatments in Randomized and Nonrandomized Studies. Journal of Educational Psychology, 66(5), 688-701.
Schuler, M., & Berger, J.O. (2017). Machine Learning and Causal Inference: The Promise and Perils of Using Data as Evidence. Harvard Data Science Review, 1(1), 2-8.