path-specific effect estimation

This paper addresses bipartite structural causal inference under interference: treatment and outcome units are distinct, their relationships are encoded by a bipartite graph, and each outcome unit may be jointly influenced by multiple treatment units. Moving beyond conventional methods that impose strong parametric assumptions—such as linearity or additivity—on exposure mappings and potential outcomes, we propose a model-free framework for defining and estimating causal effects, accommodating arbitrary heterogeneity, nonlinearity, nonadditivity, and treatment interactions. From a design-based perspective, we construct an unbiased weighted estimator that integrates the bipartite graph structure with general randomized experimental designs. We derive its exact variance and establish asymptotic consistency of outcome-unit-level estimators. Our theory identifies a nontrivial positivity condition jointly determined by network topology, experimental design, and estimator form. The method is empirically validated using data on the economic impact of high-speed rail construction.

This study addresses the identifiability of interventional effects under complex causal structures. It proposes a unified identification framework by directly interpreting single-world intervention graphs (SWIGs) as joint representations of observational and interventional distributions, thereby transcending their conventional role as mere bridges to potential outcomes. Integrating SWIGs with do-calculus and structured probabilistic modeling, the approach not only recovers classical results such as backdoor adjustment but also substantially extends the applicability of front-door criteria to more intricate scenarios. This advancement provides a more scalable theoretical foundation for identifying causal effects under general intervention structures.

This study addresses the challenge of identifying and estimating causal effects under network interference, where an individual’s treatment may spill over and affect others’ outcomes. The authors propose a solution based on a linear outcome model that yields unbiased and consistent estimates of both binary and continuous treatment effects when the interference structure is known or partially known. The approach accommodates both fixed and random interference network specifications and innovatively eliminates interference-induced bias while remaining compatible with standard linear regression software. It also conveniently allows for the incorporation of random effects and heteroskedasticity- and autocorrelation-consistent (HAC) standard errors. Numerical simulations and empirical analyses demonstrate the method’s effectiveness in bias correction and practical applicability.

This paper addresses causal inference in network experiments subject to interference. We propose a purely design-based, model-agnostic weighted least squares framework. Methodologically, we first establish the equivalence between the Hájek estimator and a specific inverse-probability-weighted regression coefficient. Second, we develop a bias-corrected network-robust covariance adjustment that ensures design-based validity of standard errors under arbitrary regression misspecification. Theoretically, our estimator is consistent and asymptotically normal. Simulations and empirical applications demonstrate stable confidence interval coverage exceeding 95%. Our approach balances practical implementability, flexible incorporation of covariates, and design-based robustness—offering a new paradigm for causal inference in network experiments that unifies theoretical rigor with empirical applicability.

Estimating causal effects from observational data requires selecting appropriate control and instrumental variables that satisfy causal identification conditions—a challenging task often reliant on strong domain knowledge or ad hoc assumptions. Method: This paper proposes the first end-to-end joint learning framework that automatically identifies valid combinations of control and instrumental variables. Grounded in conditional independence testing, the method integrates nonparametric dependence measures with structural search optimization, ensuring statistical consistency in variable selection under mild regularity conditions. Contribution/Results: Unlike conventional approaches requiring prespecified variable sets or strong prior assumptions, our framework is fully data-driven. In simulations, it achieves significantly higher variable identification accuracy. Empirically, applied to the Job Corps study, its estimated treatment effect closely aligns with results from the randomized controlled trial—demonstrating both validity and robustness in real-world causal inference.

This paper addresses the concurrent presence of three systematic biases in causal inference: interference (where an individual’s treatment affects others’ outcomes), unmeasured confounding, and lack of transportability across populations. We propose the first unified weighted sensitivity analysis framework that jointly quantifies the impact of all three biases on causal effect estimation. Our approach introduces interpretable sensitivity parameters and employs a weighting-based estimation strategy that accommodates unmeasured confounding while explicitly modeling interference structures and constraints on cross-population extrapolation. Empirical evaluations across multiple real-world settings demonstrate that the method robustly assesses bias magnitude and enhances the credibility of causal estimates. It provides an interpretable, scalable tool for causal inference in complex, dependent environments—such as social networks and public health interventions—where traditional assumptions of independence and identifiability fail.

This study addresses the challenge of efficiently estimating causal effects under confounding when experimental budgets are limited. The authors propose a novel approach that integrates instrumental variable regression with Gaussian graphical models, leveraging prior knowledge of partial joint distributions to optimize the allocation between fully observed samples and partially observed data (e.g., only \(X_{12}\)). Under a fixed budget constraint, this method analytically derives the optimal sampling scheme that minimizes the asymptotic variance of the causal effect estimator—a solution not previously available in closed form. Theoretical analysis demonstrates that the proposed allocation significantly reduces both the total budget and the number of complete observations required to detect non-zero causal effects. Empirical validation in automotive analytics and drug discovery underscores the method’s practical utility alongside its theoretical contributions.

This paper addresses causal effect estimation under unobserved confounding in a multi-domain setting, where causal effects are heterogeneous across domains, treatment and outcome variables are continuous, and observed variables—including proxies—are discrete or categorical. Leveraging the assumption that latent confounder structure is identifiable via proxy variables, we establish the first nonparametric identifiability of causal effects in a target domain where only proxies—not the confounders themselves—are observed. We propose two consistent estimators, derive their asymptotic normality, and construct valid confidence intervals. Our theoretical results are validated through simulation studies and applied to an empirical analysis of how website rankings affect consumer choice, enabling cross-domain causal transfer and precise inference.

Existing causal models struggle to distinguish between the immediate and persistent effects of interventions in time-dynamic systems, particularly when such interventions alter the system’s equilibrium behavior. This work proposes a novel paradigm grounded in system and state representations, integrating causal directed acyclic graphs, the potential outcomes framework, and dynamic systems theory. By introducing an equilibrium-state assumption and employing state-space modeling, the study reformulates the causal inference framework to better capture temporal dynamics. It innovatively defines an equilibrium-oriented “zero effect” concept and combines it with a strategic selection of time points to enable valid identification of time-varying causal parameters. The approach establishes clear criteria for categorizing causal effects under dynamic interventions, substantially enhancing the interpretability and practical utility of causal inference in equilibrium analysis.