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Inferencia post-selección para efectos causales después del descubrimiento causal

Ting-Hsuan Chang1, Zijian Guo2, Daniel Malinsky1

  • 1Department of Biostatistics, Columbia University.

Biometrika
|January 30, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio introduce un novedoso método de inferencia post-selección para algoritmos de descubrimiento causal. Asegura intervalos de confianza precisos para los efectos causales, incluso después de la selección del modelo, mediante el uso de remuestreo y cribado.

Palabras clave:
inferencia post-seleccióndescubrimiento causalefectos causalesintervalos de confianzaremuestreocribado

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Área de la Ciencia:

  • Estadística
  • Aprendizaje Automático
  • Inferencia Causal

Sus antecedentes:

  • Los algoritmos de descubrimiento causal basados en restricciones identifican modelos causales gráficos utilizando pruebas de independencia condicional.
  • Estos modelos informan la estimación del efecto causal, pero enfrentan desafíos con la inferencia válida post-selección.
  • El uso ingenuo de datos tanto para la selección del modelo como para la estimación produce intervalos de confianza inválidos.

Objetivo del estudio:

  • Desarrollar un método de inferencia post-selección para el descubrimiento causal que proporcione una cobertura asintóticamente correcta para los parámetros de efectos causales.
  • Abordar el problema de los intervalos de confianza inválidos que surgen del uso doble de datos en el descubrimiento y la estimación causal.
  • Garantizar que las afirmaciones inferenciales sean válidas para los efectos a nivel poblacional, no para cantidades dependientes de los datos.

Principales métodos:

  • Se propone un procedimiento de remuestreo y cribado, que realiza el descubrimiento causal múltiples veces con estadísticas intermedias aleatorizadas.
  • Las estimaciones de efectos causales y los conjuntos de confianza se construyen uniendo los resultados individuales basados en gráficos.
  • El enfoque se demuestra utilizando el algoritmo PC para grafos acíclicos dirigidos y distribuciones gaussianas multivariadas.

Principales resultados:

  • El método propuesto logra una cobertura asintóticamente correcta para el parámetro del efecto causal verdadero.
  • Los conjuntos de confianza garantizan ser válidos para un efecto fijo a nivel poblacional.
  • El enfoque es general y modular, aplicable a varios algoritmos de descubrimiento y familias de distribuciones.

Conclusiones:

  • La técnica de inferencia post-selección desarrollada ofrece un método estadísticamente sólido para la estimación de efectos causales después de la selección del modelo.
  • Este enfoque mejora la fiabilidad de la inferencia causal en presencia de incertidumbre sobre la estructura del modelo.
  • La modularidad del método permite una amplia aplicabilidad en diferentes marcos de descubrimiento causal.