Spurious Correlation: A Causal Interpretation*

By Herbert A. Simon. 1954

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning (2019, background): “Spurious correlations, or misinterpretations of existing correlation, can occur between two processes (e.g. correlation between player-enemy distance and saliency), and human observers are susceptible to identifying spurious correlations (Simon, 1954).”
Individual differences in need for cognition and decision-making competence among leaders (2011, unknown): “Traditional economic theories of behavior have assumed that people integrate all available information to rationally determine the utility of decision outcomes (Nash, 1950; Simon, 1954; Von Neumann & Morgenstern, 1944).”
Towards Explainable Neural-Symbolic Visual Reasoning (2019, background): “Considering that additional variables and equations must be introduced in order to test whether a correlation between two variables is genuine or spurious [Simon, 1954], being selective is less straight-forward for connectionist models than for symbolic ones.”
Bayesian Artificial Intelligence (2004, methodology): “There are equivalent alternative methods, such as one due to Simon and Blalock [257, 21] and ordinary least squares regression.”
Complementary and supplementary fit: a theoretical and empirical integration. (2004, background): “From this perspective, the effects of psychological need fulfillment on attitudes are spurious and should become insignificant once value congruence is controlled (Simon, 1954).”
Mediational Inferences in Organizational Research (2008, background): “Summing up these last two lessons from Hyman (1955), we see that the choice of which variables to include and how long and detailed a sequence of relationships to model are critical decision points for researchers.”
An Extended Class of Instrumental Variables for the Estimation of Causal Effects (2011, unknown): “Simon, 1953, 1954; Strotz and Wold, 1960; Fisher 1966).”
Causal inference (2011, methodology): “1For a related analysis under the rubric of “spurious correlation” see Simon [32].”
The Book of Why (1983, background): “Conspicuously absent: any fruit containing vitamin C. (Source: Photograph by Herbert Ponting, courtesy of Canterbury Museum, New Zealand.)”
Probabilistic arithmetic (1989, background): “1) and [757]) in order to study effects of product terms in path models.”
Comparing Regression Coefficients Between Models using Logit and Probit: A New Method (2010, background): “Interest in such genuine or “true” coefficients has a long history in the social sciences and is usually associated with the elaboration procedure in crosstabulation suggested by Lazarsfeld (1955, 1958; Kendall and Lazarsfeld 1950; cf. Simon 1954 for partial product moment correlations).”