SearcharxivSearch

arXiv · 2607.25709

On the magnitude, sign and ranking of recanting-twin path-specific effects

Abstract

The framework of recanting twin path-specific effects has recently been propose to address the issue of intermediate confounding in causal mediation analysis, enabling the decomposition of the average treatment effect into identifiable fine-grained path-specific effects (PSEs). An open question, however, is the extent to which recanting-twin PSEs reflect the direction and relative magnitude of their corresponding natural PSEs. In this paper, we systematically characterize the behaviors of the recanting-twin PSEs in terms of magnitude, sign and ranking, benchmarking against natural PSEs. To achieve this, we first derive non-parametric, identifiable upper and lower bounds for the absolute difference between recanting-twin and natural PSEs in binary outcome settings. These bounds provide a practical way to assess whether discrepancies in magnitude between the two types of effects are substantial, thereby facilitating the use of recanting-twin PSEs as informative approximations of their natural counterparts. A simulation study is then conducted to evaluate frequency of disagreements in sign and ranking between recanting-twin and natural effects in non-linear, non-monotonic scenarios. Across four scenarios and 320 numerical settings, we find that disagreement ranges from 15% to 56% for ranking and from 5% to 43% for sign. Disagreement is strongly influenced by the non-observable correlation between counterfactual values of the intermediate confounder, suggesting that caution is needed when interpreting the magnitude and ranking of recanting-twin PSEs, and motivating future work to identify conditions under which the two types of PSEs yield consistent conclusions when non-monotonicity presents.

Explore related subjects

Keep this discovery

BibTeXRIS

Tran Trong Khoi Le, Pham Hien Trang Tu, Nhat Long Ngo, Tat-Thang Vo. 2026-07-28. On the magnitude, sign and ranking of recanting-twin path-specific effects. https://arxiv.org/abs/2607.25709

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Surprise Reduction and Nullification in Bayesian and Inverse Bayesian Inference under Ambiguous Prediction-Error Attribution

In non-stationary environments, prediction errors may signal environmental change or transient outliers, and adaptive systems must track such changes without overreacting to outliers. We distinguish surprise reduction, which updates beliefs to fit observations, from surprise nullification, which weakens constraints imposed by the predictive structure, and formalize both within Bayesian and inverse Bayesian (BIB) inference. Belief and likelihood updates are derived from variational objectives sharing a nullification strength, determined endogenously by minimizing surprise under the candidate post-update predictive distribution. In the Gaussian case, nullification expands belief and likelihood variances by a common factor relative to standard Bayesian updating, leaving the ratio unchanged. BIB thus defers attribution of the prediction error, committing to neither latent-state change nor observation-process uncertainty. The nullification strength is carried over as a candidate and is maintained or released according to the predictive surprise of the next observation. In a mean estimation task with outliers and changepoints, no scanned parameter setting of a Sage-Husa-type adaptive Kalman filter, fixed-strength BIB variant, or belief-forgetting-only variant outperforms BIB in both changepoint tracking and post-outlier stability. An oracle-informed reduced Bayesian model tracks changepoints better but is less stable after outliers. Although BIB maintains no explicit hypotheses about changepoints or outliers, it generates event-dependent dynamics. The learning rate increases after changepoints, whereas after outliers, nullification is released, and this increase is suppressed. Deferring attribution and letting subsequent observations differentiate the responses may constitute a principle of adaptive inference in non-stationary environments.

stat.ME

Generalized Ridge Refitting for the Lasso and Prediction Improvement Bounds

We study a class of Lasso based estimators obtained by applying a quadratic correction on the Lasso equicorrelation set. The penalty matrix determines both the magnitude and geometry of the correction and contains, among other cases, the isotropic Lasso--Ridge correction, least squares refitting, Gram proportional interpolation between the Lasso and least squares, and coordinate specific penalties. We first derive a closed form representation and isolate the positive gain component of the resulting prediction improvement. We then control the remaining stochastic linear term in expectation by localizing the random signed equicorrelation model around a deterministic reference support. This yields a finite sample expectation bound that explicitly accounts for the randomness induced by Lasso model selection. The resulting decomposition provides a unified framework for understanding when Lasso based quadratic corrections can improve prediction.

stat.ME

Discretization in covariate-adaptive randomization: gains and losses

Covariate-adaptive randomization(CAR) is widely implemented in clinical trials to balance prognostic covariates across treatment arms. Continuous covariates are often discretized into strata in practice, yet their consequences are not clearly understood. This paper provides a comprehensive study of the impact of discretization on both the CAR design process and the inferential results thereafter. We establish the asymptotic properties of both imbalance measures and treatment effect estimators under discretized and non-discretized settings. Practical recommendations are given on when and how discretization should be employed. We show that discretization in design is generally recommended, as it enhances robustness against model misspecification. However, if the true model is known, the most efficient strategy is to balance covariates according to that model in the design. The theoretical results are corroborated by extensive simulation studies and an empirical application to a diabetes trial dataset. Together, the results clarify the gains and losses of discretization in CAR and pave the way for learning impact of discretization to other designs and beyond.

stat.ME