---
title: "Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2606.30500v1"
permalink: "https://feed.astralor.com/items/itm_ef8edfe2da4ad2ab5ef4f348"
published: "2026-06-29T16:03:39.000Z"
score: 48
channel: "tech"
tags: ["Conformal Inference","Doubly Robust Estimation","Causal Inference","Time series analysis","Adaptive Inference"]
---

# Doubly Robust Adaptive Conformal Inference for Causal Effects Under Temporal Dependence

## At a glance

This paper proposes Doubly Robust Adaptive Conformal Inference (DR-ACI), a method for constructing prediction intervals for doubly robust pseudo-outcomes under temporal dependence. It combines the strengths of doubly robust estimation and adaptive conformal inference to effectively handle structured dependencies in time series, providing interval estimates for causal effects with finite-sample coverage guarantees. Experiments validate the robustness and effectiveness of the method across various temporal dependence scenarios.

## Key points

- Proposes DR-ACI integrating doubly robust estimation with adaptive conformal inference for temporally dependent data
- Provides prediction intervals for causal effects with finite-sample coverage guarantees, requiring no additional model assumptions
- Validates effectiveness and robustness on synthetic and real-world time series data

## Full text

We propose doubly robust adaptive conformal inference (DR-ACI), which constructs prediction intervals for doubly robust pseudo-outcomes under temporal dependence.

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