Embedded is the show for people who love gadgets. Making them, breaking them, and everything in between. Weekly interviews with engineers, educators, and enthusiasts. Find the show, blog, and more at embedded.fm.
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Innhold levert av Linear Digressions, Ben Jaffe, and Katie Malone. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Linear Digressions, Ben Jaffe, and Katie Malone eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.player.fm/legal.
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Causal Trees
MP3•Episoder hjem
Manage episode 261379384 series 2527355
Innhold levert av Linear Digressions, Ben Jaffe, and Katie Malone. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Linear Digressions, Ben Jaffe, and Katie Malone eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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 episoder
MP3•Episoder hjem
Manage episode 261379384 series 2527355
Innhold levert av Linear Digressions, Ben Jaffe, and Katie Malone. Alt podcastinnhold, inkludert episoder, grafikk og podcastbeskrivelser, lastes opp og leveres direkte av Linear Digressions, Ben Jaffe, and Katie Malone eller deres podcastplattformpartner. Hvis du tror at noen bruker det opphavsrettsbeskyttede verket ditt uten din tillatelse, kan du følge prosessen skissert her https://no.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 episoder
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