Talk by Guido Imbens on Thursday Dec 12 at 11:00 in Lab 42, L3.36
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Sara Magliacane
Dec 9, 2024, 5:59:00 AM12/9/24
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Hi all,
Sorry for posting something out of the usual scope of the Amsterdam Causality Meeting, but I thought this might be interesting to you. I'm very happy to announce that we will have a talk on Thursday December 12 at 11:00 by Guido Imbens. The talk is open to the public. Here are the details:
Date and location: 12 December at 11:00-12:00 in room L3.36 at Lab42 (third floor)
Speaker: Guido Imbens (Stanford)
Title: Experimental Design in Marketplaces
Abstract: Classical Randomized Controlled Trials (RCTs), or A/B tests, are designed to draw causal inferences about a population of units, for example, individuals, plots of land or visits to a website. A key assumption underlying a standard RCT is the absence of interactions between units, or the stable unit treatment value assumption. Modern experimentation, however, is often conducted in settings characterized by complex interactions between units, sometimes in online marketplaces. Such interactions can invalidate the standard estimators and make classical experimental designs ineffective. Although the presence of interference forces us to make untestable assumptions on the nature of the interactions even under randomization, sophisticated experimental designs can ameliorate the dependence on such assumptions. One key feature common to many new experimental designs is the presence of multiple layers of randomization within the same experiment. We discuss a novel experimental design, called Multiple Randomization Designs or MRDs, that provides a general framework for such experiments. Through these complex designs, we can study questions about causal effects in the presence of interference that cannot be answered by classical RCTs.
Cheers!
Sara
Date and location: 12 December at 11:00-12:00 in room L3.36 at Lab42 (third floor)
Speaker: Guido Imbens (Stanford)
Title: Experimental Design in Marketplaces
Abstract: Classical Randomized Controlled Trials (RCTs), or A/B tests, are designed to draw causal inferences about a population of units, for example, individuals, plots of land or visits to a website. A key assumption underlying a standard RCT is the absence of interactions between units, or the stable unit treatment value assumption. Modern experimentation, however, is often conducted in settings characterized by complex interactions between units, sometimes in online marketplaces. Such interactions can invalidate the standard estimators and make classical experimental designs ineffective. Although the presence of interference forces us to make untestable assumptions on the nature of the interactions even under randomization, sophisticated experimental designs can ameliorate the dependence on such assumptions. One key feature common to many new experimental designs is the presence of multiple layers of randomization within the same experiment. We discuss a novel experimental design, called Multiple Randomization Designs or MRDs, that provides a general framework for such experiments. Through these complex designs, we can study questions about causal effects in the presence of interference that cannot be answered by classical RCTs.
Cheers!
Sara
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