We study off-policy learning (OPL) in contextual bandits, which plays a key role in a wide range of real-world applications such as recommendation systems and online advertising. Typical OPL in contextual bandits assumes an unconstrained environment where a policy can select the same item infinitely. However, in many practical applications, including coupon allocation and e-commerce, limited supply constrains items through budget limits on distributed coupons or inventory restrictions on products. In these settings, greedily selecting the item with the highest expected reward for the current user may lead to early depletion of that item, making it unavailable for future users who could potentially generate higher expected rewards. As a result, OPL methods that are optimal in unconstrained settings may become suboptimal in limited supply settings. To address the issue, we provide a theoretical analysis showing that conventional greedy OPL approaches may fail to maximize the policy performance, and demonstrate that policies with superior performance must exist in limited supply settings. Based on this insight, we introduce a novel method called Off-Policy learning with Limited Supply (OPLS). Rather than simply selecting the item with the highest expected reward, OPLS focuses on items with relatively higher expected rewards compared to the other users, enabling more efficient allocation of items with limited supply. Our empirical results on both synthetic and real-world datasets show that OPLS outperforms existing OPL methods in contextual bandit problems with limited supply.

We study the novel problem of future off-policy evaluation (F-OPE) and learning (F-OPL) for estimating and optimizing the future value of policies in non-stationary environments, where distributions vary over time. In e-commerce recommendations, for instance, our goal is often to estimate and optimize the policy value for the upcoming month using data collected by an old policy in the previous month. A critical challenge is that data related to the future environment is not observed in the historical data. Existing methods assume stationarity or depend on restrictive reward-modeling assumptions, leading to significant bias. To address these limitations, we propose a novel estimator named Off-Policy Estimator for the Future Value (OPFV), designed for accurately estimating policy values at any future time point. The key feature of OPFV is its ability to leverage the useful structure within time-series data. While future data might not be present in the historical log, we can leverage, for example, seasonal, weekly, or holiday effects that are consistent in both the historical and future data. Our estimator is the first to exploit these time-related structures via a new type of importance weighting, enabling effective F-OPE. Theoretical analysis identifies the conditions under which OPFV becomes low-bias. In addition, we extend our estimator to develop a new policy-gradient method to proactively learn a good future policy using only historical data. Empirical results show that our methods substantially outperform existing methods in estimating and optimizing the future policy value under non-stationarity for various experimental setups.

Recommender systems are inherently decision-making systems, taking actions that have consequences for the world around them.Some consequences might be desirable (for example, growing the user base for an online platform), others might be unintended (for example, amplifying inequality among item providers).In order to reason about these consequences, we need to resort to methods from the literature on causal inference.Whilst this research area has seen a growing interest in recent years, there is an abundance of open research questions from how we should model large-scale recommender systems in such causal frameworks, to what the limitations are for causal identifiability in general settings, and how we can properly handle confounding variables.The CONSEQUENCES workshop series aims to bring together researchers and practitioners who are interested in this research topic, and wish to help shape its future.

Recommender systems are inherently decision-making systems, taking actions that have consequences for the world around them. Some consequences might be desirable (for example, growing the user base for an online platform), others might be unintended (for example, amplifying inequality among item providers). In order to reason about these consequences, we need to resort to methods from the literature on causal inference. Whilst this research area has seen a growing interest in recent years, there is an abundance of open research questions from how we should model large-scale recommender systems in such causal frameworks, to what the limitations are for causal identifiability in general settings, and how we can properly handle confounding variables. The CONSEQUENCES workshop series aims to bring together researchers and practitioners who are interested in this research topic, and wish to help shape its future.

We explore off-policy evaluation and learning (OPE/L) in contextual combinatorial bandits (CCB), where a policy selects a subset in the action space. For example, it might choose a set of furniture pieces (a bed and a drawer) from available items (bed, drawer, chair, etc.) for interior design sales. This setting is widespread in fields such as recommender systems and healthcare, yet OPE/L of CCB remains unexplored in the relevant literature. Typical OPE/L methods such as regression and importance sampling can be applied to the CCB problem, however, they face significant challenges due to high bias or variance, exacerbated by the exponential growth in the number of available subsets. To address these challenges, we introduce a concept of factored action space, which allows us to decompose each subset into binary indicators. This formulation allows us to distinguish between the “main effect” derived from the main actions, and the “residual effect”, originating from the supplemental actions, facilitating more effective OPE. Specifically, our estimator, called OPCB, leverages an importance sampling-based approach to unbiasedly estimate the main effect, while employing regression-based approach to deal with the residual effect with low variance. OPCB achieves substantial variance reduction compared to conventional importance sampling methods and bias reduction relative to regression methods under certain conditions, as illustrated in our theoretical analysis. Experiments demonstrate OPCB’s superior performance over typical methods in both OPE and OPL.

We study off-policy evaluation (OPE) in the problem of slate contextual bandits where a policy selects multi-dimensional actions known as slates. This problem is widespread in recommender systems, search engines, marketing, to medical applications, however, the typical Inverse Propensity Scoring (IPS) estimator suffers from substantial variance due to large action spaces, making effective OPE a significant challenge. The PseudoInverse (PI) estimator has been introduced to mitigate the variance issue by assuming linearity in the reward function, but this can result in significant bias as this assumption is hard-to-verify from observed data and is often substantially violated. To address the limitations of previous estimators, we develop a novel estimator for OPE of slate bandits, called Latent IPS (LIPS), which defines importance weights in a low-dimensional slate abstraction space where we optimize slate abstractions to minimize the bias and variance of LIPS in a data-driven way. By doing so, LIPS can substantially reduce the variance of IPS without imposing restrictive assumptions on the reward function structure like linearity. Through empirical evaluation, we demonstrate that LIPS substantially outperforms existing estimators, particularly in scenarios with non-linear rewards and large slate spaces.

Short- and long-term outcomes of an algorithm often differ, with damaging downstream effects. A known example is a click-bait algorithm, which may increase short-term clicks but damage long-term user engagement. A possible solution to estimate the long-term outcome is to run an online experiment or A/B test for the potential algorithms, but it takes months or even longer to observe the long-term outcomes of interest, making the algorithm selection process unacceptably slow. This work thus studies the problem of feasibly yet accurately estimating the long-term outcome of an algorithm using only historical and short-term experiment data. Existing approaches to this problem either need a restrictive assumption about the short-term outcomes called surrogacy or cannot effectively use short-term outcomes, which is inefficient. Therefore, we propose a new framework called Long-term Off-Policy Evaluation (LOPE), which is based on reward function decomposition. LOPE works under a more relaxed assumption than surrogacy and effectively leverages short-term rewards to substantially reduce the variance. Synthetic experiments show that LOPE outperforms existing approaches particularly when surrogacy is severely violated and the long-term reward is noisy. In addition, real-world experiments on large-scale A/B test data collected on a music streaming platform show that LOPE can estimate the long-term outcome of actual algorithms more accurately than existing feasible methods.

Typical recommendation and ranking methods aim to optimize the satisfaction of users, but they are often oblivious to their impact on the items (e.g., products, jobs, news, video) and their providers. However, there has been a growing understanding that the latter is crucial to consider for a wide range of applications, since it determines the utility of those being recommended. Prior approaches to fairness-aware recommendation optimize a regularized objective to balance user satisfaction and item fairness based on some notion such as exposure fairness. These existing methods have been shown to be effective in controlling fairness, however, most of them are computationally inefficient, limiting their applications to only unrealistically small-scale situations. This indeed implies that the literature does not yet provide a solution to enable a flexible control of exposure in the industry-scale recommender systems where millions of users and items exist. To enable a computationally efficient exposure control even for such large-scale systems, this work develops a scalable, fast, and fair method called exposure-aware ADMM (exADMM ). exADMM is based on implicit alternating least squares (iALS), a conventional scalable algorithm for collaborative filtering, but optimizes a regularized objective to achieve a flexible control of accuracy-fairness tradeoff. A particular technical challenge in developing exADMM is the fact that the fairness regularizer destroys the separability of optimization subproblems for users and items, which is an essential property to ensure the scalability of iALS. Therefore, we develop a set of optimization tools to enable yet scalable fairness control with provable convergence guarantees as a basis of our algorithm. Extensive experiments performed on three recommendation datasets demonstrate that exADMM enables a far more flexible fairness control than the vanilla version of iALS, while being much more computationally efficient than existing fairness-aware recommendation methods.

Ranking interfaces are everywhere in online platforms. There is thus an ever growing interest in their Off-Policy Evaluation (OPE), aiming towards an accurate performance evaluation of ranking policies using logged data. A de-facto approach for OPE is Inverse Propensity Scoring (IPS), which provides an unbiased and consistent value estimate. However, it becomes extremely inaccurate in the ranking setup due to its high variance under large action spaces. To deal with this problem, previous studies assume either independent or cascade user behavior, resulting in some ranking versions of IPS. While these estimators are somewhat effective in reducing the variance, all existing estimators apply a single universal assumption to every user, causing excessive bias and variance. Therefore, this work explores a far more general formulation where user behavior is diverse and can vary depending on the user context. We show that the resulting estimator, which we call Adaptive IPS (AIPS), can be unbiased under any complex user behavior. Moreover, AIPS achieves the minimum variance among all unbiased estimators based on IPS. We further develop a procedure to identify the appropriate user behavior model to minimize the mean squared error (MSE) of AIPS in a data-driven fashion. Extensive experiments demonstrate that the empirical accuracy improvement can be significant, enabling effective OPE of ranking systems even under diverse user behavior.

Recommender systems make algorithmic decisions about what will be shown to whom, billions of times every day across the web. These decisions have consequences that can often be far-reaching. Indeed, users that are exposed to certain items, might be convinced to explore interests that are novel to them. At the same time, exposure is often linked to economic incentives for the item producer, which platform-level metrics will be impacted by as well. Feedback loops in existing systems also imply that algorithmic decisions made by the model itself, have an impact on the data future model iterations will be trained and evaluated on.

Rankings have become the primary interface in two-sided online markets. Many have noted that the rankings not only affect the satisfaction of the users (e.g., customers, listeners, employers, travelers), but that the position in the ranking allocates exposure -- and thus economic opportunity -- to the ranked items (e.g., articles, products, songs, job seekers, restaurants, hotels). This has raised questions of fairness to the items, and most existing works have addressed fairness by explicitly linking item exposure to item relevance. However, we argue that any particular choice of such a link function may be difficult to defend, and we show that the resulting rankings can still be unfair. To avoid these shortcomings, we develop a new axiomatic approach that is rooted in principles of fair division. This not only avoids the need to choose a link function, but also more meaningfully quantifies the impact on the items beyond exposure. Our axioms of envy-freeness and dominance over uniform ranking postulate that for a fair ranking policy every item should prefer their own rank allocation over that of any other item, and that no item should be actively disadvantaged by the rankings. To compute ranking policies that are fair according to these axioms, we propose a new ranking objective related to the Nash Social Welfare. We show that the solution has guarantees regarding its envy-freeness, its dominance over uniform rankings for every item, and its Pareto optimality. In contrast, we show that conventional exposure-based fairness can produce large amounts of envy and have a highly disparate impact on the items. Beyond these theoretical results, we illustrate empirically how our framework controls the trade-off between impact-based individual item fairness and user utility.

Counterfactual estimators enable the use of existing log data to estimate how some new target policy would have performed, if it had been used instead of the policy that logged the data. We say that those estimators work "off-policy", since the policy that logged the data is different from the target policy. In this way, counterfactual estimators enable Off-policy Evaluation (OPE) akin to an unbiased offline A/B test, as well as learning new decision-making policies through Off-policy Learning (OPL). The goal of this tutorial is to summarize Foundations, Implementations, and Recent Advances of OPE and OPL (OPE/OPL), with applications in recommendation, search, and an ever growing range of interactive systems. Specifically, we will introduce the fundamentals of OPE/OPL and provide theoretical and empirical comparisons of conventional methods. Then, we will cover emerging practical challenges such as how to handle large action spaces, distributional shift, and hyper-parameter tuning. We will then present Open Bandit Pipeline, an open-source Python software for OPE/OPL to better enable new research and applications. We will conclude the tutorial with future directions.

Recommender systems are more and more often modelled as repeated decision making processes – deciding which (ranking of) items to recommend to a given user. Each decision to recommend or rank an item has a significant impact on immediate and future user responses, long-term satisfaction or engagement with the system, and possibly valuable exposure for the item provider. This interactive and interventionist view of the recommender uncovers a plethora of unanswered research questions, as it complicates the typically adopted offline evaluation or learning procedures in the field. We need an understanding of causal inference to reason about (possibly unintended) consequences of the recommender, and a notion of counterfactuals to answer common “what if”-type questions in learning and evaluation. Advances at the intersection of these fields can foster progress in effective, efficient and fair learning and evaluation from logged data. These topics have been emerging in the Recommender Systems community for a while, but we firmly believe in the value of a dedicated forum and place to learn and exchange ideas. We welcome contributions from both academia and industry and bring together a growing community of researchers and practitioners interested in sequential decision making, offline evaluation, batch policy learning, fairness in online platforms, as well as other related tasks, such as A/B testing.

In real-world recommender systems and search engines, optimizing ranking decisions to present a ranked list of relevant items is critical. Off-policy evaluation (OPE) for ranking policies is thus gaining a growing interest because it enables performance estimation of new ranking policies using only logged data. Although OPE in contextual bandits has been studied extensively, its naive application to the ranking setting faces a critical variance issue due to the huge item space. To tackle this problem, previous studies introduce some assumptions on user behavior to make the combinatorial item space tractable. However, an unrealistic assumption may, in turn, cause serious bias. Therefore, appropriately controlling the bias-variance tradeoff by imposing a reasonable assumption is the key for success in OPE of ranking policies. To achieve a well-balanced bias-variance tradeoff, we propose the Cascade Doubly Robust estimator building on the cascade assumption, which assumes that a user interacts with items sequentially from the top position in a ranking. We show that the proposed estimator is unbiased in more cases compared to existing estimators that make stronger assumptions on user behavior. Furthermore, compared to a previous estimator based on the same cascade assumption, the proposed estimator reduces the variance by leveraging a control variate. Comprehensive experiments on both synthetic and real-world e-commerce data demonstrate that our estimator leads to more accurate OPE than existing estimators in a variety of settings.

In display ad auctions of Real-Time Bidding (RTB), a typical Demand-Side Platform (DSP) bids based on the predicted probability of click and conversion right after an ad impression. Recent studies find such a strategy is suboptimal and propose a better bidding strategy named lift-based bidding. Lift-based bidding simply bids the price according to the lift effect of the ad impression and achieves maximization of target metrics such as sales. Despite its superiority, lift-based bidding has not yet been widely accepted in the avertising industry. For one reason, lift-based bidding is less profitable f or DSP providers under the current billing rule. Second, the practical usefulness of lift-based bidding is not widely understood in the online advertising industry due to the lack of a comprehensive investigation of its impact.We here propose a practically-implementable lift-based bidding system that perfectly fits the current billing rules. We conduct extensive experiments using a real-world advertising campaign and examine the performance under various settings. We find that lift-based bidding, especially unbiased lift-based bidding is most profitable for both DSP providers and advertisers. Our ablation study highlights that lift-based bidding has a good property for currently dominant first price auctions. The results will motivate the online advertising industry to consider lift-based advertising.

A typical assumption in supervised machine learning is that the train (source) and test (target) datasets follow completely the same distribution. This assumption is, however, often violated in uncertain real-world applications, which motivates the study of learning under covariate shift. In this setting, the naive use of adaptive hyperparameter optimization methods such as Bayesian optimization does not work as desired since it does not address the distributional shift among different datasets. In this work, we consider a novel hyperparameter optimization problem under the i>multi-source covariate shift whose goal is to find the optimal hyperparameters for a target task of interest using only unlabeled data in a target task and labeled data inmultiple source tasks. To conduct efficient hyperparameter optimization for the target task, it is essential to estimate the target objective using only the available information. To this end, we construct the variance reduced estimator that unbiasedly approximates the target objective with a desirable variance property. Building on the proposed estimator, we provide a general and tractable hyperparameter optimization procedure, which works preferably in our setting with a no-regret guarantee. The experiments demonstrate that the proposed framework broadens the applications of automated hyperparameter optimization.

Counterfactual estimators enable the use of existing log data to estimate how some new target recommendation policy would have performed, if it had been used instead of the policy that logged the data. We say that those estimators work ”off-policy”, since the policy that logged the data is different from the target policy. In this way, counterfactual estimators enable Off-policy Evaluation (OPE) akin to an unbiased offline A/B test, as well as learning new recommendation policies through Off-policy Learning (OPL). The goal of this tutorial is to summarize Foundations, Implementations, and Recent Advances of OPE/OPL. Specifically, we will introduce the fundamentals of OPE/OPL and provide theoretical and empirical comparisons of conventional methods. Then, we will cover emerging practical challenges such as how to take into account combinatorial actions, distributional shift, fairness of exposure, and two-sided market structures. We will then present Open Bandit Pipeline, an open-source package for OPE/OPL, and how it can be used for both research and practical purposes. We will conclude the tutorial by presenting real-world case studies and future directions.

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems. Since many OPE estimators have been proposed and some of them have hyperparameters to be tuned, there is an emerging challenge for practitioners to select and tune OPE estimators for their specific application. Unfortunately, identifying a reliable estimator from results reported in research papers is often difficult because the current experimental procedure evaluates and compares the estimators’ performance on a narrow set of hyperparameters and evaluation policies. Therefore, it is difficult to know which estimator is safe and reliable to use. In this work, we develop Interpretable Evaluation for Offline Evaluation (IEOE), an experimental procedure to evaluate OPE estimators’ robustness to changes in hyperparameters and/or evaluation policies in an interpretable manner. Then, using the IEOE procedure, we perform extensive evaluation of a wide variety of existing estimators on Open Bandit Dataset, a large-scale public real-world dataset for OPE. We demonstrate that our procedure can evaluate the estimators’ robustness to the hyperparamter choice, helping us avoid using unsafe estimators. Finally, we apply IEOE to real-world e-commerce platform data and demonstrate how to use our protocol in practice.

Post-click conversion, a pre-defined action on a web service after a click, is an essential form of feedback, as it directly contributes to the final revenue and accurately captures user preferences for items, compared with the ambiguous click. However, naively using post-click conversions can lead to severe bias when learning or evaluating recommenders because of the selection bias between clicked and unclicked data. In this study, we address the offline evaluation problem of algorithmic recommendations with biased post-click conversions. A possible solution to address this bias is to use the inverse propensity score estimator, as it can provide an unbiased evaluation even with the selection bias. However, this estimator is known to be subject to variance and instability problems, which can be severe in the recommendation setting, as feedback is often highly sparse. To address these limitations with the previous unbiased estimator, we propose a doubly robust estimator for the ground-truth ranking performance of a given recommender. The proposed estimator is unbiased against the ground-truth ranking metric and improves the variance and estimation error tail bound of the existing unbiased estimator. Finally, to evaluate the empirical efficacy of the proposed estimator, we conduct empirical evaluations using semi-synthetic and two public real-world datasets. The results show that the proposed metric reveals a better model evaluation performance compared with existing baseline metrics, particularly in a situation with severe selection bias.

Uplift modeling aims to optimize treatment policies and is a promising method for causal-based personalization in various domains such as medicine and marketing. However, applying this method to real-world problems faces challenges such as the impossibility of validation and binary treatment limitation. The Contextual Treatment Selection (CTS) algorithm was proposed to overcome the binary treatment limitation and demonstrated state-of-the-art results. However, previous experiments have implied that CTS is cost-ineffective because it requires a large amount of training data. In this paper, we demonstrate that the estimator maximized in CTS is biased against the true metric. We then propose a variance reduced estimator based on the doubly robust estimation technique that provides unbiasedness and desirable variance. We further propose a treatment policy optimization algorithm called VAriance Reduced Treatment Selection (VARTS), which maximizes our estimator. Empirical experiments on synthetic and real-world datasets demonstrated that our method outperforms other existing methods, particularly under realistic conditions such as small sample sizes and high noise levels. These theoretical and empirical results imply that our method can overcome the critical challenges of uplift modeling and should be the first choice for optimizing personalization in various fields.

In most real-world recommender systems, the observed rating data are subject to selection bias, and the data are thus missing-not-at-random. Developing a method to facilitate the learning of a recommender with biased feedback is one of the most challenging problems, as it is widely known that naive approaches under selection bias often lead to suboptimal results. A well-established solution for the problem is using propensity scoring techniques. The propensity score is the probability of each data being observed, and unbiased performance estimation is possible by weighting each data by the inverse of its propensity. However, the performance of the propensity-based unbiased estimation approach is often affected by choice of the propensity estimation model or the high variance problem. To overcome these limitations, we propose a model-agnostic meta-learning method inspired by the asymmetric tri-training framework for unsupervised domain adaptation. The proposed method utilizes two predictors to generate data with reliable pseudo-ratings and another predictor to make the final predictions. In a theoretical analysis, a propensity-independent upper bound of the true performance metric is derived, and it is demonstrated that the proposed method can minimize this bound. We conduct comprehensive experiments using public real-world datasets. The results suggest that the previous propensity-based methods are largely affected by the choice of propensity models and the variance problem caused by the inverse propensity weighting. Moreover, we show that the proposed meta-learning method is robust to these issues and can facilitate in developing effective recommendations from biased explicit feedback.

In display advertising, predicting the conversion rate (CVR), meaning the probability that a user takes a predefined action on an advertiser's website, is a fundamental task for estimating the value of displaying an advertisement to a user. There are two main challenges in CVR prediction due to delayed feedback. First, some positive labels are not correctly observed in training data because some conversions do not occur immediately after a click. Second, delay mechanisms are not uniform among instances, meaning some positive feedback are much more frequently observed than others. It is widely acknowledged that these problems lead to severe bias in CVR prediction. To overcome these challenges, we propose two unbiased estimators: one for CVR prediction and the other for bias estimation. Subsequently, we propose a dual learning algorithm in which a CVR predictor and a bias estimator are trained in alternating fashion using only observable conversions. The proposed algorithm is the first of its kind to address the two major challenges in a theoretically sophisticated manner. Empirical evaluations using synthetic datasets demonstrate the practical value of the proposed approach.

Recommender systems widely use implicit feedback such as click data because of its general availability. Although the presence of clicks signals the users' preference to some extent, the lack of such clicks does not necessarily indicate a negative response from the users, as it is possible that the users were not exposed to the items (positive-unlabeled problem). This leads to a difficulty in predicting the users' preferences from implicit feedback. Previous studies addressed the positive-unlabeled problem by uniformly upweighting the loss for the positive feedback data or estimating the confidence of each data having relevance information via the EM-algorithm. However, these methods failed to address the missing-not-at-random problem in which popular or frequently recommended items are more likely to be clicked than other items even if a user does not have a considerable interest in them. To overcome these limitations, we first define an ideal loss function to be optimized to realize recommendations that maximize the relevance and propose an unbiased estimator for the ideal loss. Subsequently, we analyze the variance of the proposed unbiased estimator and further propose a clipped estimator that includes the unbiased estimator as a special case. We demonstrate that the clipped estimator is expected to improve the performance of the recommender system, by considering the bias-variance trade-off. We conduct semi-synthetic and real-world experiments and demonstrate that the proposed method largely outperforms the baselines. In particular, the proposed method works better for less popular items that are less frequently observed in the training data. The findings indicate that the proposed method can better achieve the objective of recommending items with the highest relevance.

Uplift modeling aims to optimize treatment allocation by predicting the net effect of a treatment on each individual (ITE) and is expected to achieve causal-based personalization in medicine, marketing, etc. This approach needs specialized methods to train and evaluate ITE prediction models because the true ITE is unobservable. The conventional uplift modeling requires data to be gathered through randomized controlled trials (RCTs), on the other hand, for non-RCT data, the transformed outcome (TO) is commonly used as an unbiased estimator of ITE. However, it is often impossible to conduct RCTs for ethical and economic reasons, and, in observational data, the unbiasedness of TO is based on the unrealistic assumption that the propensity score of each individual is given. In this paper, we theoretically and quantitatively show TO becomes an unreliable proxy ITE when the propensity score estimator is biased or has a large degree of heterogeneity. We then propose a novel proxy outcome, Switch Doubly Robust, turning on and off the effect of propensity score estimator on the outcome prediction models. We theoretically prove SDR achieves better bias-variance trade-off as a proxy ITE than TO and develop novel prediction (SDRM) and evaluation (SDR-MSE) methods. Furthermore, we experimentally show our methods outperformed existing approaches on synthetic datasets. In addition, we applied them to the Right Heart Catheterization dataset and discovered 20% of patients are actually curable, even though the conventional causal inference methods only showed the average treatment effect is negative. We anticipate our methods to be a standard practice of uplift modeling for observational data and lead to optimized personalization in various fields.