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To Explain or to Predict? Galit Shmueli. Abstract. Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description.
Predictions vs. Causal Inferences. Even in academic fields like economics and other social sciences, the concepts of predictive power and explanatory power are often conflated — models showing high explanatory power are often assumed to be highly predictive.
In our discussion of explanation and prediction, we have emphasized the differences between the two approaches in order to make it clear what shifting toward a more predictive psychology would entail, and what benefits such a shift would provide.
It is tempting to think that the only difference between explanations and predictions is that one looks back and tells us how or why things happened as they did, and the other looks forward and tells us how or why certain things will (or are likely to) happen.
The purpose of this article is to clarify the distinction between explanatory and predictive modeling, to discuss its sources, and to reveal the practical implications of the distinction to each step in the modeling process.
To Explain or to Predict? Galit Shmueli Abstract. Statistical modeling is a powerful tool for developing and testing theories by way of causal explanation, prediction, and description. In many disciplines there is near-exclusive use of statistical modeling for causal ex- planation and the assumption that models with high explanatory power are.
Researchers have argued that the boundaries between prediction and explanation are far less sharp than traditionally conceived: identifying causal effects provides a basis for...
My quick explanation is AIC is best for prediction as it is asymptotically equivalent to cross-validation. BIC is best for explanation as it is allows consistent estimation of the underlying data generating process.
This post elegantly brings to light the crucial distinction between explanation and prediction in both traditional scientific studies and the evolving field of AI and data science. It underlines the essential understanding that accurate prediction does not necessarily equate to a deep understanding of the causal mechanisms at play.
Prediction vs. Explanation: With the advent of Big Data and data mining, statistical methods like regression and CART have been repurposed to use as tools in predictive modeling. When statistical models are used as a tool of research, the goal is to explain relationships in a dataset, and make inference beyond the specific data to shed light on ...