DE LA ECONOMETRÍA AL RAZONAMIENTO CAUSAL: INTELIGENCIA ARTIFICIAL Y FORMACIÓN ECONÓMICA EN TIEMPOS DE BIG DATA
DOI :
https://doi.org/10.37135/kai.03.16.10Mots-clés :
Inteligencia artificial, Big Data, Inferencia causal, Credibilidad empírica, Econometría aplicada, Formación en economíaRésumé
La rápida expansión de la inteligencia artificial (IA) y del Big Data transforma el mercado laboral y la investigación en economía, profundizando los cuestionamientos sobre la credibilidad de los resultados empíricos, el énfasis en la causalidad y la formación universitaria vigentel. El artículo analiza estos cambios como parte de la credibility revolution en economía empírica, destacando sus aportes en diseño de investigación y sus límites frente a sistemas complejos y dinámicos. A partir de una revisión crítica de la literatura reciente, se argumenta que más datos y mayor capacidad computacional no garantizan mejores inferencias sin marcos conceptuales adecuados. Se propone una integración entre enfoques basados en diseño, modelos causales estructurales y aprendizaje automático, y se discuten sus implicaciones para la enseñanza de la economía y la empleabilidad en la era de la IA.
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