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♪ (música) ♪

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- [Narradora] Bienvenidos[br]a Nobel Conversations.

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En este episodio,[br]Josh Angrist y Guido Imbens

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están con Isaiah Andrews[br]para discutir y discrepar

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sobre el papel[br]del aprendizaje automático

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en la econometría aplicada.

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- [Isaiah] Entonces, por supuesto[br]que hay muchos temas

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en los que ustedes están muy de acuerdo,[br]pero me gustaría pasar a uno

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en el que tal vez[br]tengan algunas discrepancias.

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Me gustaría escuchar[br]algunas de sus opiniones

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sobre el aprendizaje automático

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y el papel que está desempeñando[br]y va a desempeñar en la economía.

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- [Guido] He consultado algunos datos[br]como el propietario----------------------.

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Vemos que no hay[br]ningún documento publicado allí.

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Se hizo un experimento[br]sobre algún algoritmo de búsqueda

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y la cuestión era...

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se trataba de clasificar cosas[br]y cambiar la clasificación.

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Y estaba más o menos claro[br]que iba a haber mucha heterogeneidad.

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Si buscas, digamos, una foto de Britney Spears --

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realmente no importa dónde la clasifiques-----------------------------------[br]porque vas a encontrar lo que estás buscando,

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ya sea que la pongas en la primera,[br]segunda o tercera posición del ranking.

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Pero si estás buscando[br]el mejor libro de econometría,

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si pones tu libro en primer lugar[br]o en el décimo,

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eso va a suponer una gran diferencia

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en la frecuencia[br]con la que la gente va a hacer clic en él.

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Así que ahí...

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[Josh] ¿Por qué necesito[br]el aprendizaje automático para descubrir eso?

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Parece que --porque puedo descubrirlo simplemente.-----------------------

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- [Guido] En general --

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- [Josh] Había un montón de posibles...

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- Quieres pensar en que hay un montón de características de los artículos,

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que quieres entender lo que impulsa la heterogeneidad en el efecto de --

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- Pero solo estás prediciendo.

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En cierto sentido, estás resolviendo[br]un problema de marketing.

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- No, es un efecto causal,

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- Es causal, pero no tiene contenido científico.

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Piensa en...

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- No, pero hay cosas similares[br]en el ámbito médico.

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Si haces un experimento,

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puedes estar muy interesado[br]en si el tratamiento funciona

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para algunos grupos o no.

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Y tienes un montón[br]de características individuales,

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y quieres buscar sistemáticamente...

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- Sí. Soy escéptico sobre esa...

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esa especie de idea de que hay[br]un efecto causal personal

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que me debería importar

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y que el aprendizaje automático[br]puede descubrirlo de alguna manera que sea útil.

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Así que piensa en --

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he hecho[br]un montón de trabajo en las escuelas,

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ir a, digamos, una escuela charter,-------------------------------------------------

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una escuela privada financiada[br]con fondos públicos,

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efectivamente, que es libre de estructurar[br]su propio plan de estudios para el contexto allí.

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Algunos tipos de escuelas chárter[br]generan espectaculares ganancias de rendimiento

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y en el conjunto de datos[br]que produce ese resultado,

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tengo un montón de covariables.

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Así que tengo las puntuaciones de referencia

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y tengo los antecedentes familiares,

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la educación de los padres,[br]el sexo del niño, la raza del niño.

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Y, bueno, en cuanto reúno[br]media docena de ellas,

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tengo un espacio de muy alta dimensión.

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Definitivamente me interesan[br]las características del curso

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de ese efecto del tratamiento,

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como por ejemplo si es mejor para las personas[br]que provienen de familias con menores ingresos.

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Me cuesta creer que haya una aplicación para la versión de muy alta dimensión de eso,

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en la que descubrí[br]que para los niños no blancos

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que tienen ingresos familiares altos

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pero puntuaciones de referencia en el tercer cuartil

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y que solo fueron a la escuela pública[br]en el tercer grado pero no en el sexto.

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