Estimating Dyadic Treatment Effects with Unknown Confounders

• URL: http://arxiv.org/abs/2405.16547v1
• Date: Sun, 26 May 2024 12:32:14 GMT
• Title: Estimating Dyadic Treatment Effects with Unknown Confounders
• Authors: Tadao Hoshino, Takahide Yanagi,
• Abstract summary: We propose a statistical inference method for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution, our approach allows for the presence of unobserved confounding factors. We apply our method to international trade data to assess the impact of free trade agreements on bilateral trade flows.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a statistical inference method for assessing treatment effects with dyadic data. Under the assumption that the treatments follow an exchangeable distribution, our approach allows for the presence of any unobserved confounding factors that potentially cause endogeneity of treatment choice without requiring additional information other than the treatments and outcomes. Building on the literature of graphon estimation in network data analysis, we propose a neighborhood kernel smoothing method for estimating dyadic average treatment effects. We also develop a permutation inference method for testing the sharp null hypothesis. Under certain regularity conditions, we derive the rate of convergence of the proposed estimator and demonstrate the size control property of our test. We apply our method to international trade data to assess the impact of free trade agreements on bilateral trade flows.

Related papers

• Conformal Diffusion Models for Individual Treatment Effect Estimation and Inference [6.406853903837333]
  Individual treatment effect offers the most granular measure of treatment effect on an individual level. We propose a novel conformal diffusion model-based approach that addresses those intricate challenges.
  arXiv  Detail & Related papers  (2024-08-02T21:35:08Z)
• Interpretable Causal Inference for Analyzing Wearable, Sensor, and Distributional Data [62.56890808004615]
  We develop an interpretable method for distributional data analysis that ensures trustworthy and robust decision-making. We demonstrate ADD MALTS' utility by studying the effectiveness of continuous glucose monitors in mitigating diabetes risks.
  arXiv  Detail & Related papers  (2023-12-17T00:42:42Z)
• Uncertainty Quantification in Heterogeneous Treatment Effect Estimation with Gaussian-Process-Based Partially Linear Model [2.1212179660694104]
  Estimating heterogeneous treatment effects across individuals has attracted growing attention as a statistical tool for performing critical decision-making. We propose a Bayesian inference framework that quantifies the uncertainty in treatment effect estimation to support decision-making in a relatively small sample size setting.
  arXiv  Detail & Related papers  (2023-12-16T12:42:28Z)
• Benchmarking Bayesian Causal Discovery Methods for Downstream Treatment Effect Estimation [137.3520153445413]
  A notable gap exists in the evaluation of causal discovery methods, where insufficient emphasis is placed on downstream inference. We evaluate seven established baseline causal discovery methods including a newly proposed method based on GFlowNets. The results of our study demonstrate that some of the algorithms studied are able to effectively capture a wide range of useful and diverse ATE modes.
  arXiv  Detail & Related papers  (2023-07-11T02:58:10Z)
• Bayesian Counterfactual Mean Embeddings and Off-Policy Evaluation [10.75801980090826]
  We present three novel Bayesian methods to estimate the expectation of the ultimate treatment effect. These methods differ on the source of uncertainty considered and allow for combining two sources of data. We generalize these ideas to the off-policy evaluation framework.
  arXiv  Detail & Related papers  (2022-11-02T23:39:36Z)
• Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization [0.0]
  We propose a method for estimation and inference for bounds for heterogeneous causal effect parameters. The method provides conditional effect bounds as functions of policy relevant pre-treatment variables. We use a flexible debiased/double machine learning approach that can accommodate non-linear functional forms and high-dimensional confounders.
  arXiv  Detail & Related papers  (2022-09-09T14:42:03Z)
• Assessment of Treatment Effect Estimators for Heavy-Tailed Data [70.72363097550483]
  A central obstacle in the objective assessment of treatment effect (TE) estimators in randomized control trials (RCTs) is the lack of ground truth (or validation set) to test their performance. We provide a novel cross-validation-like methodology to address this challenge. We evaluate our methodology across 709 RCTs implemented in the Amazon supply chain.
  arXiv  Detail & Related papers  (2021-12-14T17:53:01Z)
• Efficient Causal Inference from Combined Observational and Interventional Data through Causal Reductions [68.6505592770171]
  Unobserved confounding is one of the main challenges when estimating causal effects. We propose a novel causal reduction method that replaces an arbitrary number of possibly high-dimensional latent confounders. We propose a learning algorithm to estimate the parameterized reduced model jointly from observational and interventional data.
  arXiv  Detail & Related papers  (2021-03-08T14:29:07Z)
• Enabling Counterfactual Survival Analysis with Balanced Representations [64.17342727357618]
  Survival data are frequently encountered across diverse medical applications, i.e., drug development, risk profiling, and clinical trials. We propose a theoretically grounded unified framework for counterfactual inference applicable to survival outcomes.
  arXiv  Detail & Related papers  (2020-06-14T01:15:00Z)
• Interpretable Off-Policy Evaluation in Reinforcement Learning by Highlighting Influential Transitions [48.91284724066349]
  Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education. Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding. We develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates.
  arXiv  Detail & Related papers  (2020-02-10T00:26:43Z)

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.