2020. Counterfactual prediction for bundle treatment H Zou, P Cui, B Li, Z Shen, J Ma, H Yang, Y He Advances in Neural Information Processing Systems 33, 19705-19715 , 2020 Systematic racial differences in case characteristics, many unobservable, make this a difficult question to answer directly. IJCAI, 2018. In words, if the bundle (q u (p t, x t, u), z u (p t, x t, u)) could be bought with income x v, u at price p v, then the bundle (q u (p v, x v, u), z u (p v, x v, u)) should not have been strictly cheaper at income x t, u and price p t. 4. In particular, the prediction of the difference that interventions make requires a grasp of causal inference that is a distinct skill set from that of the prediction of a trend, as proponents of the potential outcomes approach have correctly pointed out. . Therefore, both the counterfactual and the actual situation must be forecast, which increases uncertainty about the actual extent of harm. State and local governments imposed social distancing measures in March and April 2020 to contain the spread of the novel coronavirus disease (COVID-19). Stata and cvs files data have been . Shengyu Zhang, Dong Yao, Zhou Zhao, Tat-Seng Chua, and Fei Wu. Contrastive Learning for Debiased . does not, then the second (untreated) twin serves as the counterfactual. Well-being indexes in 1986: actual and counterfactual predictions We use the estimated parameters of the utility function to simulate the well-being level of the families in our sample in 1986, i.e., just before the application of TRA86, and compare it with the well-being level they would have reached in the counterfactual scenario where the . lowing counterfactual: "By how much would real income have had to increase in Japan during the autarky years of 1851-1853 to afford the consumption bundle the economy could have obtained if it were engaged in interna-tional trade during that period?" Using alter-native approaches to estimating Japan's GDP during the final years of autarky, we . (Paper) (Dataset) Zheyean Shen, Peng Cui, Jiashuo Liu, Tong Zhang, Bo Li and Zhitang Chen. 09:00. CauseRec: Counterfactual User Sequence Synthesis for Sequential Recommendation. In essence, the "prediction" serves the role of a counterfactual scenario guided by the data. Counterfactual Prediction for Bundle Treatment (2020NeurIPS) Adversarial Counterfactual Learning and Evaluation for Recommender System (2020NeurIPS) Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback (2020NeurIPS) Learning Stable Graphs from Multiple Environments with Selection Bias (2020KDD) Paul R. Rosenbaum. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based on speculation and convenient but indefensible model assumptions rather than empirical evidence. Once the treatment starts, we simply extend the "prediction" and compare it with the actual data. ABSTRACT. I am sharing with you some of the research topics regarding Machine Learning that you can choose for your research proposal for the thesis work of MS, or Ph.D. VSR algorithm utilize the low dimensional latent structure of bundle treatment. Accurately predicting the counterfactual outcome of different treatments is of paramount importance for decision makers across many domains, such as healthcare [4] and marketing [5]. THE RESULT Likewise, the attribution, after the fact, of a certain outcome to an intervention only . This paper proposes a method for estimating consumer preferences among discrete choices, where the consumer chooses at most one product in a category, but selects from multiple categories in parallel. It allows for identification (sometimes partial) of the counterfactual distribution, in particular, identifying average treatment effects and quantile treatment effects. Fuli Feng, Jizhi Zhang, Xiangnan He, Hanwang Zhang, and Tat-Seng Chua. Abstract Are minorities treated differently by the legal system? Clinical prediction models (CPMs) can inform decision making about treatment initiation, which requires predicted risks assuming no treatment is given. Inferences about counterfactuals are essential for prediction, answering "what if" questions, and estimating causal effects. However, when the counterfactuals posed are too far from the data at hand, conclusions drawn from well-specified statistical analyses become based on speculation and convenient but indefensible model assumptions rather than empirical evidence. For example, an increase in P1 alone will increase τ (because V ΄>0) and thus make consumers less likely to desire treatment 1. In this paper, we estimate whether judges differ from each other in how they sentence minorities, avoiding potential bias from unobservable case characteristics by exploiting the random assignment of cases . KDD, 2018. You can help correct errors and omissions. This paper reviews the evolution of clinical neuroscience pertaining to disorders of consciousness, while exploring key ethical challenges surrounding the care of patients with these disorders. In its simplest form, the comparative syllogism derives a conclusion about the effectiveness of a factor (e.g. . Abstract. Counterfactual Prediction for Bundle Treatment (2020NeurIPS) Adversarial Counterfactual Learning and Evaluation for Recommender System (2020NeurIPS) Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback (2020NeurIPS) Learning Stable Graphs from Multiple Environments with Selection Bias (2020KDD) Vermont Senator Bernie Sanders spent a large part of the last week defending earlier statements he made regarding Fidel Castro and Cuba. Her preferences about product attributes as well as her price sensitivity vary across products and may be correlated across . Counterfactual Prediction for Bundle Treatments. These measures included bans on large socia. •Challenges of counterfactual prediction for bundle treatment: •Confounding bias in the observational data •High dimensional property and complexity of bundle treatment 19 We propose Variational Sample Re-weighting (VSR) algorithm for counterfactual prediction. The propensity score is the conditional probability of assignment to a particular treatment given a vector of observed covariates. Examples include classification and regression problems, and estimating treatment effects in randomized trials or observational data. In this paper, we estimate whether judges differ from each other in how they sentence minorities, avoiding potential bias from unobservable case characteristics by exploiting the random assignment of cases to judges. The issue of fairness arises in such problems where some covariates or treatments are . Machine Learning Research Topic ideas for MS, or Ph.D. Algorithmic Decision Making with Conditional Fairness (with Renzhe Xu, Peng Cui, Kun Kuang, Lingjun Zhou, Zheyan Shen and Wei Cui), KDD , 2020, CCF中国计算机学会A类 Degree. Counterfactual Prediction for Bundle Treatment (with Hao Zou, Peng Cui, Zheyan Shen, Jianxin Ma, Hongxia Yang and Yue He), NeurIPS, 2020 Algorithmic Decision Making with Conditional Fairness (with. Causal inference has a central role in public health; the determination that an association is causal indicates the possibility for intervention. In this thesis, we aim to empower deep learning with graph-structured data, by facilitating deep learning models to take graphs as the input, the output, and the prior. Fluid-limiting treatment strategies among sepsis patients in the ICU: a retrospective causal analysis . Progressive Generative Hashing for Image Retrieval. . We review and comment on the long-used guidelines for interpreting evidence as supporting a causal association and contrast them with the potential outcomes framework that encourages thinking in terms of causes that are interventions. Google Scholar; Index Terms. Consider some measurable set S ∈ Ω. The primary challenge of estimating the mortality rates in the other counterfactual RCT arms from observational data is confounding—that is, the tendency for patients to receive differential amounts of fluids because they had different . Applications of machine learning to machine fault diagnosis: A review and roadmap. Abstract. The difference is the causal role of the treatment. The difference in the average outcome between There is another, more recent paper published in July of this year, suggesting, more or less, that race is not a cause of health outcomes - or . Cuban education comes as a bundle. Donald B. Rubin. Counterfactual Prediction for Bundle Treatments. But the counterfactual is still vague: any practical way to implement the reduction in BMI, whether by exercise, diet, stomach surgery, or liposuction may well have different effects on longevity. Counterfactual Prediction for Bundle Treatment. In the same vein, if we had a group of essentially similar subjects, then the average outcome for those who did not take the treatment would serve as the counterfactual for those who did. Evaluates the predictions of a causal model that outputs treatment outcomes w.r.t. The consumer's utility is additive in the different categories. With set-wise (exact-k, slate, combinatorial) recommendation, we aim to optimize the whole set of items to recommend while taking the dependency among items into consideration.This enables us to model, for example, the substitution relationship of items, i.e., a customer tends to purchase only one item in the same category, in contrast to the top-k recommendation in which the . In this work, we propose a novel link prediction method that enhances graph learning by the counterfactual inference. His proposal is motivated by the insight that policy-makers address counterfactual questions that, as such, can only be answered properly in well-developed causal models.9 The scholars who propose RCT replication and cross-country regressions as strate- gies to improve the external validity of causal effects fail to take this point fully on board. cal problem of counterfactual predictions of demand and welfare. Bo Li, Zheyan Shen, Jianxin Ma, Hongxia Yang, and Yue He. . Corrections. EXAM first endows each subject with a common artificial budget and lets her use the budget to purchase the most preferred (highest WTP) bundle of treatment-assignment probabilities given their prices. Systematic racial differences in case characteristics, many unobservable, make this a difficult question to answer directly. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence Macao, 10-16 August 2019 To this purpose, we propose a "digital twin" of patients modeling the human body as a whole and providing a panoramic view over individuals' conditions.Methods: We propose a . The two papers overlap, with the first containing all the proofs and technical material and the second having more pedagogical material and examples. As such, these shape restrictions establish the analogs of Slutsky conditions—the cornerstone of classical de-mand analysis—for binary choice under general unobserved heterogeneity and income effects. In this paper, we consider the problem of fair statistical inference involving outcome variables. All material on this site has been provided by the respective publishers and authors. Causally Regularized Learning on Data with Agnostic Bias. 2019. Among the various forms of treatment specification, bundle treatment has been widely adopted in many scenarios, such as recommendation systems and online marketing. Final goods trade in the counterfactual experiment is (8) C hn j ' = π ^ hn j f π hn j f 1 + τ hn j f ' α n j I n '. Yukuo Cen , Jing Zhang, Hongxia Yang and Jie Tang, Trust Prediction in Alibaba E-Commerce Platform, ACM Transactions on Knowledge and Data Engineering. But central to all counterfactual causal accounts of racial discrimination is the notion that there is a solid state race in units (individuals, neighborhoods, etc. Abstract: We are in the middle of a remarkable rise in the use and capability of artificial intelligence. In essence, the "prediction" serves the role of a counterfactual scenario guided by the data. Introduction. In general: (6.10) ΄ ΄ d τ = V ΄ ( Y − P 1) d P 1 − V ΄ ( Y − P 2) d P 2. Maciej Besta. (Paper) Yue He, Zheyan Shen, Peng Cui . What . Stable Prediction across Unknown Environments. NeurIPS, 2020. The difference is the causal role of the treatment. 2021. The counterfactual Leontief inverse is then simply B ' = (I − A') −1. Much of this growth has been fueled by the success of deep learning architectures . (paper) Fluid-limiting treatment strategies among sepsis patients in the ICU: a retrospective causal analysis . In essence, the "prediction" serves the role of a counterfactual scenario guided by the data. To do so, this paper proposes and empirically implements an experimental design that improves subjects' welfare while producing similar experimental information as typical RCTs do. • Counterfactuals are also required to forecast the e ects of policies that . In ACL-IJCNLP Findings. Towards Non-I.I.D. The model is flexible enough to handle multiple outcome variables, multidimensional heterogeneity, and multiple clusters. Due to the fundamental lack of counterfactual data, the CATEs are computed for bins of a given column. ACL. TKDE 2019. The role of serotonin in human brain function remains elusive due, at least in part, to our inability to measure rapidly the local concentration of this neurotransmitter. The counterfactual value added flows corresponding to Eqs. Counterfactual Predictions under Runtime Confounding Amanda Coston, Edward Kennedy, Alexandra Chouldechova Learning Loss for Test-Time Augmentation Ildoo Kim, Younghoon Kim, Sungwoong Kim Balanced Meta-Softmax for Long-Tailed Visual Recognition Jiawei Ren, Cunjun Yu, shunan sheng, Xiao Ma, Haiyu Zhao, Shuai Yi, hongsheng Li This function then applies a fklearn-like evaluator on top of the aggregated dataframe. Case for 'assessing damages in antitrust actions' Counterfactual Prediction with Deep Instrumental Variables Networks. The standard treatment-response setting begins with a population J and a set of potential treatments T. As the leading cause of death in hospitals in the United States, improved detection and treatment of sepsis is a leading worldwide healthcare priority ().Annually, ~1.7 million adults in the United States are hospitalized for sepsis with ~270,000 deaths ().Furthermore, sepsis is a major economic burden as it is the most costly U.S. inpatient disease with more than $24 billion spent in 2013 . of the analysis of treatment response, but it is easy to define concepts in those terms and helpful to do so.2 I show how here and introduce the type of counterfactual prediction problem studied in this article. Abstract Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields. Bounds on counterfactual demands. Bounds on the Jensen Gap, and Implications for Mean-Concentrated Distributions. The prices are personalized so that each treatment is cheaper for subjects with better predicted effects of the treatment. Both large and small sample . Enabling High-Performance Large-Scale Irregular Computations. The goal of this paper is to alleviate this dilemma. 2. 2021 c. Empowering Language Understanding with Counterfactual Reasoning. Only after 1970 does it start converging back to the counterfactual (very rapidly). (paper) Yuqing Ma, Yue He, Fan Ding, Sheng Hu, Jun Li, Xianglong Liu. ACM Multimedia, 2018. We argue that . The effect of counterfactual outcomes on both dopamine (Kishida et al, 2016) and serotonin (Figure 4) suggests that it is crucial to perform the analysis of action encoding (betting more or less) at different bet levels as a bet of 0% could result in large foregone gains (ie, counterfactual losses), while a bet of 100% could result in large . Google Scholar; Xiang Gao, Meera Sitharam, and Adrian E. Roitberg. 357 PDF View 2 excerpts, references methods Rubin and Pearl offered approaches to causal effect estimation and Lewis and Pearl offered theories of counterfactual conditionals. We used fast-scan cyclic . Yang, H., Lu, Q., Qiu, A.X. Pattern Recognition, 2020. The difference is the causal role of the treatment. Estimating counterfactual outcome of different treatments from observational data is an important problem to assist decision making in a variety of fields. . KDD, 2017. Stable Learning via Differentiated Variable Decorrelation. Degree. ), an objective fact about the units that can be isolated after stripping away all confounders. Counterfactual Prediction for Bundle Treatment (with Hao Zou, Peng Cui, Zheyan Shen, Jianxin Ma, Hongxia Yang and Yue He), NeurIPS, 2020, CCF中国计算机学会A类. The opposite is true about an increase in P2. Randomized controlled trials (RCTs) determine the fate of numerous people, giving rise to a long-standing ethical dilemma. It creates counterfactual links from the observed ones, and our method learns. a treatment or an exposure) on a certain . Counterfactual prediction for bundle treatment H Zou, P Cui, B Li, Z Shen, J Ma, H Yang, Y He Advances in Neural Information Processing Systems 33, 19705-19715 , 2020 As of 15 March 2021, the novel coronavirus SARS-CoV-2 had spread throughout every continent, with over 100 million cases and 2.5 million deaths reported worldwide.1 Case numbers in the African continent continue to rise, and until widespread deployment of an effective vaccine, there is a critical reliance on non-pharmaceutical interventions (NPI) to reduce transmission. I'm on my way back from the World Epi Congress in Anchorage, where causation and causal inference have been central topics of discussion. Kun Kuang, Peng Cui, Bo Li, Shiqiang Yang. My research in these three directions has opened new frontiers for deep learning research: (1) Learning from graphs with deep learning. , result as (9) VA ihn k, j ' = β i k I n ' α n j b ih k, j ' π ^ hn j f π hn j f 1 + τ . When requesting a correction, please mention this item's handle: RePEc:arx:papers:2009.03379.See general information about how to correct material in RePEc.. For technical questions regarding this item, or to correct its authors, title, abstract . Large graphs are behind many problems in today's computing landscape. A change in a single price will have an unambiguous effect. Inferences about counterfactuals are essential for prediction, answering "what if" questions, and estimating causal effects. Others, counterfactual theorists, believe it is a special kind of counterfactual dependence between distinct existences (Lewis, 1973a), and others hold that causes raise the probability of their effects in a special way (Eells, 1991; Suppes, 1970). The primary challenge of estimating the mortality rates in the other counterfactual RCT arms from observational data is confounding—that is, the tendency for patients to receive differential amounts of fluids because they had different . Improving Data Analytics with Fast and Adaptive Regularization. The difference is the causal role of the treatment. Once the treatment starts, we simply extend the "prediction" and compare it with the actual data. Authors: Jason Hartford, Greg Lewis, Kevin Leyton-Brown, Matt Taddy. The growing sizes of such graphs, reaching 70 trillion edges recently, require unprecedented amounts of compute power, storage, and energy. TKDE 2019. As the prediction tasks being different, the treatment specification can also be in various forms, for instance, binary, continuous, multi-level and bundle. Objective: Modern medicine needs to shift from a wait and react, curative discipline to a preventative, interdisciplinary science aiming at providing personalized, systemic, and precise treatment plans to patients. 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) BigMine Workshop, 2016. . What . NeurIPS, 2020. I wrote previously about a paper (Hernan and Taubman 2008) suggesting that obesity is not a cause of mortality. Cuban education comes as a bundle. Counterfactual Prediction for Bundle Treatment (2020NeurIPS) Adversarial Counterfactual Learning and Evaluation for Recommender System (2020NeurIPS) Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback (2020NeurIPS) Learning Stable Graphs from Multiple Environments with Selection Bias (2020KDD) However, this is challenging since CPMs are usually derived using data sets where patients received treatment, often initiated postbaseline as "trea … Cuba deviated from the counterfactual by a sizable margin. Counterfactual Prediction for Bundle Treatment (with Hao Zou, Peng Cui, Zheyan Shen, Jianxin Ma, Hongxia Yang and Yue He), NeurIPS, 2020, CCF中国计算机学会A类. In Cuba, Meera Sitharam, and estimating treatment effects in randomized or. Material on this site has been provided by counterfactual prediction for bundle treatment data examples include classification and regression,! 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