Deep reinforcement learning (DRL) has gained significant attention in autonomous systems, yet its black-box nature and lack of explainability hinder user trust in safety-critical domains such as autonomous driving. Existing experience replay approaches enhance sample efficiency but often fail to capture the internal causality of training data, leading to a convoluted training process that is difficult for humans to explain. In this work, we introduce Experience Replay with Causal Inference (ERCI), an explainable approach that integrates time series representation and causal inference to offer human-aligned explanations for DRL. Specifically, ERCI 1) introduces a novel multivariate time series representation to extract explainable Time Series Causal Factors (TSCF) from experimental data and 2) leverages internal causality in TSCFs with causal inference as a crucial standard for experience replay in DRL training. We evaluate ERCI using multiple baseline algorithms across diverse environments. Results show that ERCI provides human-aligned explanations and further improves sample efficiency through enhanced explainability. Notably, ERCI outperforms other state-of-the-art approaches by 15% in average performance, highlighting its effectiveness and generalizability.

This paper investigates $\sim_{\mathcal{L}_3}$-identifiability, a form of complete counterfactual identifiability within the Pearl Causal Hierarchy (PCH) framework, ensuring that all Structural Causal Models (SCMs) satisfying the given assumptions provide consistent answers to all causal questions. To simplify this problem, we introduce exogenous isomorphism and propose $\sim_{\mathrm{EI}}$-identifiability, reflecting the strength of model identifiability required for $\sim_{\mathcal{L}_3}$-identifiability. We explore sufficient assumptions for achieving $\sim_{\mathrm{EI}}$-identifiability in two special classes of SCMs: Bijective SCMs (BSCMs), based on counterfactual transport, and Triangular Monotonic SCMs (TM-SCMs), which extend $\sim_{\mathcal{L}_2}$-identifiability. Our results unify and generalize existing theories, providing theoretical guarantees for practical applications. Finally, we leverage neural TM-SCMs to address the consistency problem in counterfactual reasoning, with experiments validating both the effectiveness of our method and the correctness of the theory.

We propose an importance sampling method for tractable and efficient estimation of counterfactual expressions in general settings, named Exogenous Matching. By minimizing a common upper bound of counterfactual estimators, we transform the variance minimization problem into a conditional distribution learning problem, enabling its integration with existing conditional distribution modeling approaches. We validate the theoretical results through experiments under various types and settings of Structural Causal Models (SCMs) and demonstrate the outperformance on counterfactual estimation tasks compared to other existing importance sampling methods. We also explore the impact of injecting structural prior knowledge (counterfactual Markov boundaries) on the results. Finally, we apply this method to identifiable proxy SCMs and demonstrate the unbiasedness of the estimates, empirically illustrating the applicability of the method to practical scenarios.