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Causal Trees
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Manage episode 261379384 series 2527355
Konten disediakan oleh Linear Digressions, Ben Jaffe, and Katie Malone. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Linear Digressions, Ben Jaffe, and Katie Malone atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.
What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answers) but economists Susan Athey and Guido Imbens are on the case. This episodes explores their algorithm for recursively partitioning a dataset to find heterogeneous treatment effects, or for you ML nerds, applying decision trees to causal inference problems. It’s not a free lunch, but for those (like us!) who love crossover topics, causal trees are a smart approach from one field hopping the fence to another. Relevant links: https://www.pnas.org/content/113/27/7353
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291 episode
MP3•Beranda episode
Manage episode 261379384 series 2527355
Konten disediakan oleh Linear Digressions, Ben Jaffe, and Katie Malone. Semua konten podcast termasuk episode, grafik, dan deskripsi podcast diunggah dan disediakan langsung oleh Linear Digressions, Ben Jaffe, and Katie Malone atau mitra platform podcast mereka. Jika Anda yakin seseorang menggunakan karya berhak cipta Anda tanpa izin, Anda dapat mengikuti proses yang diuraikan di sini https://id.player.fm/legal.
What do you get when you combine the causal inference needs of econometrics with the data-driven methodology of machine learning? Usually these two don’t go well together (deriving causal conclusions from naive data methods leads to biased answers) but economists Susan Athey and Guido Imbens are on the case. This episodes explores their algorithm for recursively partitioning a dataset to find heterogeneous treatment effects, or for you ML nerds, applying decision trees to causal inference problems. It’s not a free lunch, but for those (like us!) who love crossover topics, causal trees are a smart approach from one field hopping the fence to another. Relevant links: https://www.pnas.org/content/113/27/7353
…
continue reading
291 episode
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