Algorithmic recourse (AR) has made significant progress by identifying small perturbations in input features that can alter predictions, which provide a data-centric approach to understand decisions from diverse black-box models on the Web. Towards the feasibility issue, i.e., whether the recoursed examples provides actionable and reliable recommendations to end-users, causal algorithmic recourse have incorporated structural causal model (SCM) to preserve the realistic constraints among input features. For instance, preserving structural causal knowledge between "age" and "educational level" can avoid generating samples with decreasing age and increasing educational level. However, previous causal AR methods suffer from the requirement of prior structural causal knowledge, e.g., prior causal graph or the whole SCM, which restricts the realistic application of causal AR methods.

Recently, the bottleneck of multi-object tracking is shifting from detection performance to association performance. However, research on association algorithms requires a large number of identity labels, which are more expensive than detection labels. To circumvent the need for identity labels, we propose a Self-supervised Re-identification module for online joint Multi-Object Tracking (SR-MOT). Specifically, we design an appearance discriminator to judge identities based solely on detection hypotheses and then associate the same identity with the final trajectory. To train the discriminator without using identity labels, we construct negative pairs by the detections that appear in the same video frame, as they definitely belong to different identities. Positive pairs are naturally constructed through several useful data augmentation strategies at the box level. In addition, our proposed method balances conflicting detection and re-ID tasks by using different output features and dynamically adjusts detection and re-ID loss weights based on the information content of the loss distribution to promote balance between the two tasks from the feature level and optimization methods. In our evaluation on the MOT Challenge benchmark, we show that our SR-MOT performs comparably to supervised methods and is significantly superior to other unsupervised methods. Our proposed method provides a practical solution for multi-object tracking without the need for identity labels, making it more accessible for real-world applications.

In the era of big data, decision-making in social networks may introduce bias due to interconnected individuals. For instance, in peer-to-peer loan platforms on the Web, considering an individual's attributes along with those of their interconnected neighbors, including sensitive attributes, is vital for loan approval or rejection downstream. Unfortunately, conventional fairness approaches often assume independent individuals, overlooking the impact of one person's sensitive attribute on others' decisions. To fill this gap, we introduce "Interference-aware Fairness" (IAF) by defining two forms of discrimination as Self-Fairness (SF) and Peer-Fairness (PF), leveraging advances in interference analysis within causal inference. Specifically, SF and PF causally capture and distinguish discrimination stemming from an individual's sensitive attributes (with fixed neighbors' sensitive attributes) and from neighbors' sensitive attributes (with fixed self's sensitive attributes), separately. Hence, a network-informed decision model is fair only when SF and PF are satisfied simultaneously, as interventions in individuals' sensitive attributes or those of their peers both yield equivalent outcomes. To achieve IAF, we develop a deep doubly robust framework to estimate and regularize SF and PF metrics for decision models. Extensive experiments on synthetic and real-world datasets validate our proposed concepts and methods.

Driven by empirical risk minimization, machine learning algorithm tends to exploit subtle statistical correlations existing in the training environment for prediction, while the spurious correlations are unstable across environments, leading to poor generalization performance. Accordingly, the problem of the Out-of-distribution (OOD) generalization aims to exploit an invariant/stable relationship between features and outcomes that generalizes well on all possible environments. To address the spurious correlation induced by the selection bias, in this article, we propose a novel Clique-based Causal Feature Separation (CCFS) algorithm by explicitly incorporating the causal structure to identify causal features of outcome for OOD generalization. Specifically, the proposed CCFS algorithm identifies the largest clique in the learned causal skeleton. Theoretically, we guarantee that either the largest clique or the rest of the causal skeleton is exactly the set of all causal features of the outcome. Finally, we separate the causal features from the non-causal ones with a sample-reweighting decorrelator for OOD prediction. Extensive experiments validate the effectiveness of the proposed CCFS method on both causal feature identification and OOD generalization tasks.

In causal inference, it is common to select a subset of observed covariates, named the adjustment features, to be adjusted for estimating the treatment effect. For real-world applications, the abundant covariates are usually observed, which contain extra variables partially correlating to the treatment (treatment-only variables, e.g., instrumental variables) or the outcome (outcome-only variables, e.g., precision variables) besides the confounders (variables that affect both the treatment and outcome). In principle, unbiased treatment effect estimation is achieved once the adjustment features contain all the confounders. However, the performance of empirical estimations varies a lot with different extra variables. To solve this issue, variable separation/selection for treatment effect estimation has received growing attention when the extra variables contain instrumental variables and precision variables.

Learning individualized causal effect (ICE) plays a vital role in various fields of big data analysis, ranging from fine-grained policy evaluation to personalized treatment development. However, the presence of unmeasured confounders increases the difficulty of estimating ICE in real-world scenarios. A wide range of methods have been proposed to address the unmeasured confounders with the aid of instrument variable (IV), which sources from the treatment randomization. The performance of these methods relies on the well-predefined IVs that satisfy the unconfounded instruments assumption (i.e., the IVs are independent with the unmeasured confounders given observed covariates), which is untestable and leads to finding a valid IV becomes an art rather than science. In this paper, we focus on estimating the ICE with confounded instruments that violate the unconfounded instruments assumption. By considering the conditional independence between the set of confounded instruments and the outcome variable, we propose a novel method, named CVAE-IV, to generate a substitute of the unmeasured confounder with a conditional variational autoencoder. Our theoretical analysis guarantees that the generated confounder substitute will identify unbiased ICE. Extensive experiments on bias demand prediction and Mendelian randomization analysis verify the effectiveness of our method.

Unsupervised domain adaptation (UDA) attempts to transfer specific knowledge from one domain with labeled data to another domain without labels. Recently, maximum squares loss has been proposed to tackle UDA problem but it does not consider the prediction diversity which has proven beneficial to UDA. In this paper, we propose a novel normalized squares maximization (NSM) loss in which the maximum squares is normalized by the sum of squares of class sizes. The normalization term enforces the class sizes of predictions to be balanced to explicitly increase the diversity. Theoretical analysis shows that the optimal solution to NSM is one-hot vectors with balanced class sizes, i.e., NSM encourages both discriminate and diverse predictions. We further propose a robust variant of NSM, RNSM, by replacing the square loss with L2,1-norm to reduce the influence of outliers and noises. Experiments of cross-domain image classification on two benchmark datasets illustrate the effectiveness of both NSM and RNSM. RNSM achieves promising performance compared to state-of-the-art methods. The code is available at https://github.com/wj-zhang/NSM.

Beyond classical domain-specific adversarial training, a recently proposed task-specific framework has achieved a great success in single source domain adaptation by utilizing task-specific decision boundaries. However, compared to single-source-single-target setting, multi-source domain adaptation (MDA) shows more powerful capability to handle with most real-life cases. To align target domain with diverse multi-source domains using task-specific decision boundaries, we provide a deep insight of task-specific framework on MDA for the first time. Accordingly, we propose a novel task-specific multi-source domain adaptation method (TMDA) with a clustering embedded adversarial training process. Specifically, the proposed TMDA detects and refines less discriminative target representations through a max-min optimization over two adversarial task-specific classifiers. Moreover, our analysis implies that scattered multi-source representations disturb the adversarial training under the task-specific framework. To tight up the dispersed source representations, we embeds a relationship-based domain clustering into TMDA. Empirical results demonstrate that our TMDA outperforms state-of-the-art methods on toy dataset, sentiment analysis and digit classification.