Kernel methods for long term dose response curves

• URL: http://arxiv.org/abs/2201.05139v2
• Date: Tue, 31 Dec 2024 19:49:52 GMT
• Title: Kernel methods for long term dose response curves
• Authors: Rahul Singh, Hannah Zhou,
• Abstract summary: A core challenge in causal inference is how to extrapolate long term effects, of possibly continuous actions, from short term experimental data. We propose a simple nonparametric estimator based on kernel ridge regression. We extend our results to long term counterfactual distributions, proving weak convergence.
• Score: 2.2984209387877628
• License:
• Abstract: A core challenge in causal inference is how to extrapolate long term effects, of possibly continuous actions, from short term experimental data. It arises in artificial intelligence: the long term consequences of continuous actions may be of interest, yet only short term rewards may be collected in exploration. For this estimand, called the long term dose response curve, we propose a simple nonparametric estimator based on kernel ridge regression. By embedding the distribution of the short term experimental data with kernels, we derive interpretable weights for extrapolating long term effects. Our method allows actions, short term rewards, and long term rewards to be continuous in general spaces. It also allows for nonlinearity and heterogeneity in the link between short term effects and long term effects. We prove uniform consistency, with nonasymptotic error bounds reflecting the effective dimension of the data. As an application, we estimate the long term dose response curve of Project STAR, a social program which randomly assigned students to various class sizes. We extend our results to long term counterfactual distributions, proving weak convergence.

Related papers

• Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights [23.602196005738676]
  Long-term causal effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions to estimate long-term average effects.
  arXiv  Detail & Related papers  (2024-06-27T14:13:46Z)
• Relaxed Quantile Regression: Prediction Intervals for Asymmetric Noise [51.87307904567702]
  Quantile regression is a leading approach for obtaining such intervals via the empirical estimation of quantiles in the distribution of outputs. We propose Relaxed Quantile Regression (RQR), a direct alternative to quantile regression based interval construction that removes this arbitrary constraint. We demonstrate that this added flexibility results in intervals with an improvement in desirable qualities.
  arXiv  Detail & Related papers  (2024-06-05T13:36:38Z)
• A Study of Posterior Stability for Time-Series Latent Diffusion [59.41969496514184]
  We first show that posterior collapse will reduce latent diffusion to a variational autoencoder (VAE), making it less expressive. We then introduce a principled method: dependency measure, that quantifies the sensitivity of a recurrent decoder to input variables. Building on our theoretical and empirical studies, we introduce a new framework that extends latent diffusion and has a stable posterior.
  arXiv  Detail & Related papers  (2024-05-22T21:54:12Z)
• Long-term Off-Policy Evaluation and Learning [21.047613223586794]
  Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. It takes months or even longer to observe the long-term outcomes of interest, making the algorithm selection process unacceptably slow. We propose a new framework called Long-term Off-Policy Evaluation (LOPE), which is based on reward function decomposition.
  arXiv  Detail & Related papers  (2024-04-24T06:59:59Z)
• Choosing a Proxy Metric from Past Experiments [54.338884612982405]
  In many randomized experiments, the treatment effect of the long-term metric is often difficult or infeasible to measure. A common alternative is to measure several short-term proxy metrics in the hope they closely track the long-term metric. We introduce a new statistical framework to both define and construct an optimal proxy metric for use in a homogeneous population of randomized experiments.
  arXiv  Detail & Related papers  (2023-09-14T17:43:02Z)
• Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding [49.1865229301561]
  We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals. The last of our three diverse benchmarks is a novel usage of GPT-4 for observational experiments with unknown but probeable ground truth.
  arXiv  Detail & Related papers  (2023-06-15T21:42:40Z)
• Estimating long-term causal effects from short-term experiments and long-term observational data with unobserved confounding [5.854757988966379]
  We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Our long-term causal effect estimator is obtained by combining regression residuals with short-term experimental outcomes.
  arXiv  Detail & Related papers  (2023-02-21T12:22:47Z)
• On the Benefits of Large Learning Rates for Kernel Methods [110.03020563291788]
  We show that a phenomenon can be precisely characterized in the context of kernel methods. We consider the minimization of a quadratic objective in a separable Hilbert space, and show that with early stopping, the choice of learning rate influences the spectral decomposition of the obtained solution.
  arXiv  Detail & Related papers  (2022-02-28T13:01:04Z)
• Long-term Causal Inference Under Persistent Confounding via Data Combination [38.026740610259225]
  We study the identification and estimation of long-term treatment effects when both experimental and observational data are available. Since the long-term outcome is observed only after a long delay, it is not measured in the experimental data, but only recorded in the observational data.
  arXiv  Detail & Related papers  (2022-02-15T07:44:20Z)
• Long-Term Effect Estimation with Surrogate Representation [43.932546958874696]
  This work studies the problem of long-term effect where the outcome of primary interest, or primary outcome, takes months or even years to accumulate. We propose to build connections between long-term causal inference and sequential models in machine learning.
  arXiv  Detail & Related papers  (2020-08-19T03:16:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.