Our long-term causal effect estimator is obtained by combining regression residuals with short-term experimental outcomes in a specific manner to create an instrumental variable, which is then used to quantify the long-term causal effect through instrumental variable regression.
Feb 21, 2023
Sep 13, 2022 · In this talk, we study a different approach inspired by recent advances in off-policy evaluation in reinforcement learning (RL). The basic RL ...
Apr 6, 2023 · Our paper provided a method to combine short-term experiments with long-run observational data to estimate long-term causal effects even when latent ...
Feb 23, 2024 · In this article, our focus will be on addressing two questions: How to identify and test whether the long-term impact of the experiment differs from the short- ...
It's important to estimate the long-term impacts of our actions, but we often can't observe long-term outcomes within a decision-relevant time frame.
Mar 12, 2024 · Long-term effects monitoring is about understanding how a change affects user behavior, satisfaction, and your bottom line over time.
Our long-term causal effect estimator is obtained by combining regression residuals with short-term experimental data in a specific manner to create an ...
We study the estimation of long-term treatment effects through the combination of short-term experimental and long-term observational data sets.
May 9, 2024 · We introduce a statistical approach that allows for the offline evaluation of an algorithm's long-term outcomes by using only historical and short-term ...
An object of this invention is to improve the accuracy of estimating a destination in a destination estimating apparatus. A destination estimating apparatus ...