In marketing, optimizing subsidy allocation to maximize overall profits is of substantial economic importance. Prior research has employed treatment effect estimation techniques to identify subsidy-sensitive items and design corresponding allocation strategies. However, more accurate treatment effect estimations do not necessarily lead to better allocations, underscoring the critical influence of decision boundaries in decision-making. This paper argues that optimal allocation fundamentally depends on predicting the expected optimal subsidy, a challenge distinct from conventional treatment effect estimation or causal decision-making, which existing approaches fail to address. To fill this gap, we introduce a two-stage Counterfactual optimal subsidy Learning method with an Asymmetric reward (CoLA). In the first stage, we derive a coarse estimate of the expected subsidy threshold by exploiting order information and the conditional independence between expected and observed subsidies. In the second stage, we refine these estimates using an asymmetric loss function, leading to more robust predictions. Under practical budget constraints, we prioritize candidates based on their Sharpe ratios to determine the final subsidy allocation strategy. Experiments on three public datasets and an online A/B test show that our method achieves significant performance improvements, yielding the highest total profit and incremental leverage ratios.

Post-Click Conversion Rate (CVR) prediction aims to predict the probability of a conversion event occurring after a user clicks. Most CVR prediction methods use clicked events to train models and subsequently predict on both clicked and unclicked events, facing selection bias. To unbiasedly predict CVR, doubly robust (DR) learning incorporates propensity score reweighting and missing data error imputation, but with suboptimal performance under sparse click events. We theoretically demonstrate that existing DR methods face high or even unbounded bias, variance, and generalization error bound under small propensity scores from sparse click events. This motivates us to propose a new Batch-Adaptive DR (BADR) Learning method. In particular, we propose a BADR estimator, which adaptively adjusts the influence of each data batch during debiasing CVR prediction model training based on the propensity scores within that batch. We prove that the BADR estimator has bounded bias, variance, and generalization error bound, all of which are smaller than those of the DR estimator under small propensity scores, while maintaining asymptotic double robustness, i.e., achieving double robustness under a large sample size. Furthermore, we approximate the variance of the BADR estimator and derive a new batch-adaptive imputation model training loss compatible with the BADR estimator, which theoretically ensures further variance reduction during training. Our experiments on real-world datasets validate BADR’s effectiveness and rationality.

Imbalanced Time-Series Classification is a critical, yet challenging task across a spectrum of real-world applications. Previous oversampling and generative approaches primarily target the minority class and often rely on static decision boundaries or similarity-based heuristics. However, these methods overlook the underlying causal factors that govern the distinction between majority and minority classes, particularly in scenarios with ambiguous class boundaries. As a result, the generated samples may fail to enhance class separability, thereby limiting improvements in classification performance. To this end, we propose a CounterFactual Augmentation Minority Generation (CFAMG) method based on generative models that aims to discover the causal factors that determine different classes from a causality perspective. Specifically, our method first utilizes a disentangled classifier to distinguish between causal and non-causal factors. Next, we perform counterfactual intervention by replacing the causal factors of majority class samples with those from minority class samples, creating an intervened latent representation that reflects minority characteristics while preserving essential structures. Finally, the trained minority class decoder generates counterfactual minority samples that resemble real minority instances yet remain distinguishable from the original majority class. Extensive experiments demonstrate that our method outperforms state-of-the-art methods in both univariate and multivariate imbalanced time-series classification tasks. The code is published at https://github.com/WangLei-CQU/CFAMG.

Estimating the conditional average treatment effect (CATE) from observational data plays a crucial role in areas such as e-commerce, healthcare, and economics. Existing studies mainly rely on the strong ignorability assumption that there are no unmeasured confounders, whose presence cannot be tested from observational data and can invalidate any causal conclusion. In contrast, data collected from randomized controlled trials (RCT) do not suffer from confounding, but are usually limited by a small sample size. In this paper, we propose a two-stage pretraining-finetuning (TSPF) framework using both large-scale observational data and small-scale RCT data to estimate the CATE in the presence of unmeasured confounding. In the first stage, a foundational representation of covariates is trained to estimate counterfactual outcomes through large-scale observational data. In the second stage, we propose to train an augmented representation of the covariates, which is concatenated to the foundational representation obtained in the first stage to adjust for the unmeasured confounding. To avoid overfitting caused by the small-scale RCT data in the second stage, we further propose a partial parameter initialization approach, rather than training a separate network. The superiority of our approach is validated on two public datasets with extensive experiments. The code is available at https://github.com/zhouchuanCN/KDD25-TSPF.

The post-click conversion rate (CVR) prediction task aims to predict the probability of a conversion after a click, which is essential in many fields. There are two widely-recognized challenges for CVR prediction: selection bias and data sparsity. Many previous methods focus on addressing selection bias by unbiasedly estimating the ideal loss based on the doubly robust estimator, which incorporates the error imputation model and propensity model to help CVR prediction model learning. However, they struggle with unreasonable knowledge transfer between the prediction model and imputation model and inflexible network structure design under sparse data. To this end, we introduce a novel principled adaptive structure learning approach, named Adap-SL, to adaptively learn the optimal network structure, adjust the number of activated (non-zero) parameters, and determine which knowledge needs to be transferred between the prediction model and the imputation model. Specifically, we start with an over-parameterized base network, where we adaptively extract partially overlapped subnetworks for the imputation model and the prediction model. Extensive experiments are conducted on three real-world recommendation datasets, demonstrating that our method consistently improves performance while requiring fewer parameters. The code is available at https://github.com/ChunyuanZheng/sigir25-sparse-sharing.

A significant challenge in air quality index (AQI) prediction is to accurately evaluate the potential outcomes after conducting interventions in pollutant factors such as industrial emissions for each enterprise. Existed methods often suffer from spurious correlations caused by unmeasured confounders and are lack of interpretability of the model, leading to sub-optimal prediction performance. This motivates us to propose a causal AQI prediction framework (CAP) that employs a structural causal model (SCM) to characterize the causal structural variability of various AQI factors for robust AQI prediction. Specifically, we employ the front-door adjustment to explicitly eliminate unmeasured confounders by intervening in industrial emissions from the target enterprise. Meanwhile, we take industrial emissions of neighboring enterprises into account when intervening in the target enterprise and simulate the dispersion of industrial emissions through a Gaussian plume model based on meteorological factors. Experiments on two real-world datasets validate the superior performance of our model on AQI prediction compared to the state-of-the-art baselines.

In database of recommender systems, users' ratings for most items are usually missing, resulting in selection bias when users selectively choose items to rate. To address this problem, propensity-based methods, e.g., inverse propensity scoring and doubly robust, have been widely studied and applied to missing rating prediction and post-click conversion rate prediction tasks. However, have we completely eliminated the selection bias? Under what missing data mechanism can previous studies completely eliminate the selection bias and lead to unbiased learning? In this paper, following the previous literature on statistics, we first formally define three missing data mechanisms, i.e., missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR), and discuss the widespread prevalence of MNAR in recommender systems. Next, we theoretically reveal that the unbiasedness of previous propensity-based debiasing methods is valid only when data are MCAR or MAR, while it leads to biased predictions when data are MNAR. To tackle this research gap, we propose to disentangle user and item embeddings into the primary latent vector for rating prediction and the auxiliary latent vector for missing mechanism modeling. We prove the identifiablility results, and show that the proposed method can achieve unbiased learning under MNAR with imposed constraints. Extensive experiments are conducted on a semi-synthetic dataset and three real-world datasets, validating the effectiveness of our proposed method.

Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions have been proposed, including error-imputation-based (EIB), inverse-propensity-scoring (IPS), and doubly robust (DR) methods. However, these methods ignore an alternative form of bias caused by the inconsistency between the observed ratings and the users' true preferences, also known as noisy feedback or outcome measurement errors (OME), e.g., due to public opinion or low-quality data collection process. In this work, we study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data. First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios. Next, we theoretically prove the unbiasedness and generalization bound of the proposed estimators. We further propose an alternate denoising training approach to achieve unbiased learning of the prediction model under MNAR data with OME. Extensive experiments are conducted on three real-world datasets and one semi-synthetic dataset to show the effectiveness of our proposed approaches. The code is available at https://github.com/haoxuanli-pku/KDD24-OME-DR.

Effective personalized incentives can improve user experience and increase platform revenue, resulting in a win-win situation between users and e-commerce companies. Previous studies have used uplift modeling methods to estimate the conditional average treatment effects of users' incentives, and then placed the incentives by maximizing the sum of estimated treatment effects under a limited budget. However, some users will always buy whether incentives are given or not, and they will actively collect and use incentives if provided, named "Always Buyers". Identifying and predicting these "Always Buyers" and reducing incentive delivery to them can lead to a more rational incentive allocation. In this paper, we first divide users into five strata from an individual counterfactual perspective, and reveal the failure of previous uplift modeling methods to identify and predict the "Always Buyers". Then, we propose principled counterfactual identification and estimation methods and prove their unbiasedness. We further propose a counterfactual entire-space multi-task learning approach to accurately perform personalized incentive policy learning with a limited budget. We also theoretically derive a lower bound on the reward of the learned policy. Extensive experiments are conducted on three real-world datasets with two common incentive scenarios, and the results demonstrate the effectiveness of the proposed approaches.

Adverse drug reaction (ADR) prediction plays a crucial role in both health care and drug discovery for reducing patient mortality and enhancing drug safety. Recently, many studies have been devoted to effectively predict the drug-ADRs incidence rates. However, these methods either did not effectively utilize non-clinical data, i.e., physical, chemical, and biological information about the drug, or did little to establish a link between content-based and pure collaborative filtering during the training phase. In this paper, we first formulate the prediction of multi-label ADRs as a drug-ADR collaborative filtering problem, and to the best of our knowledge, this is the first work to provide extensive benchmark results of previous collaborative filtering methods on two large publicly available clinical datasets. Then, by exploiting the easy accessible drug characteristics from non-clinical data, we propose ADRNet, a generalized collaborative filtering framework combining clinical and non-clinical data for drug-ADR prediction. Specifically, ADRNet has a shallow collaborative filtering module and a deep drug representation module, which can exploit the high-dimensional drug descriptors to further guide the learning of low-dimensional ADR latent embeddings, which incorporates both the benefits of collaborative filtering and representation learning. Extensive experiments are conducted on two publicly available real-world drug-ADR clinical datasets and two non-clinical datasets to demonstrate the accuracy and efficiency of the proposed ADRNet. The code is available at https://github.com/haoxuanli-pku/ADRnet.

Recommender systems are seen as an effective tool to address information overload, but it is widely known that the presence of various biases makes direct training on large-scale observational data result in sub-optimal prediction performance. In contrast, unbiased ratings obtained from randomized controlled trials or A/B tests are considered to be the golden standard, but are costly and small in scale in reality. To exploit both types of data, recent works proposed to use unbiased ratings to correct the parameters of the propensity or imputation models trained on the biased dataset. However, the existing methods fail to obtain accurate predictions in the presence of unobserved confounding or model misspecification. In this paper, we propose a theoretically guaranteed model-agnostic balancing approach that can be applied to any existing debiasing method with the aim of combating unobserved confounding and model misspecification. The proposed approach makes full use of unbiased data by alternatively correcting model parameters learned with biased data, and adaptively learning balance coefficients of biased samples for further debiasing. Extensive real-world experiments are conducted along with the deployment of our proposal on four representative debiasing methods to demonstrate the effectiveness.