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Affordable Uplift: Supervised Randomization in Controlled Experiments
[article]
2019
arXiv
pre-print
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Training and monitoring of uplift models require experimental data. ...
To increase the cost-efficiency of experimentation and facilitate frequent data collection and model training, we introduce supervised randomization. ...
We will discuss IPW as a method that is easily integrated into model building and evaluation and discuss the doubly robust estimator as a recent extension. ...
arXiv:1910.00393v1
fatcat:je2dkoj3mje3fb6u4a6tnsdvl4
Stabilized Doubly Robust Learning for Recommendation on Data Missing Not at Random
[article]
2022
arXiv
pre-print
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In recommender systems, users always choose the favorite items to rate, which leads to data missing not at random and poses a great challenge for unbiased evaluation and learning of prediction models. ...
Currently, the doubly robust (DR) method and its variants have been widely studied and demonstrate superior performance. ...
Doubly robust estimation in missing data and causal inference models. ...
arXiv:2205.04701v2
fatcat:r2vr7rg5ofcqvkdv3z67m7f2wy
Doubly Robust Collaborative Targeted Learning for Debiased Recommendations
[article]
2022
arXiv
pre-print
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To address selection bias and confounding bias, the doubly robust (DR) method and its variants show superior performance due to the double robustness property and smaller bias under inaccurate propensity ...
In recommender systems, the collected data always contains various biases and leads to the challenge of accurate predictions. ...
rate prediction [7, 45] , and uplift modeling [23, 25, 26] . ...
arXiv:2203.10258v2
fatcat:v2w5fch7lbdtpozuzmnrxtckdq
A general framework for causal classification
[article]
2020
arXiv
pre-print
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Experiments have shown two instantiations of the framework work for causal classification and for uplift (causal heterogeneity) modelling, and are competitive with the other uplift (causal heterogeneity ...
We discuss the conditions when causal classification can be resolved by uplift (and causal heterogeneity) modelling methods. ...
Theorem 2, improves other uplift (causal heterogeneity) modelling methods. ...
arXiv:2003.11940v3
fatcat:ftcaz7hhezeohency6ahdmirla
On the Opportunity of Causal Learning in Recommendation Systems: Foundation, Estimation, Prediction and Challenges
[article]
2022
arXiv
pre-print
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Finally, we formalize many debiasing and prediction tasks in RS, and summarize the statistical and machine learning-based causal estimation methods, expecting to provide new research opportunities and ...
Many causal-based prediction and debiasing studies rarely discuss the causal interpretation of various biases and the rationality of the corresponding causal assumptions. ...
[Wang et al., 2019a] propose the doubly robust (DR) method and the joint learning optimization technique. ...
arXiv:2201.06716v3
fatcat:2mn5piulfraqbcfvn2d4rpgzby
Evaluation Methods and Measures for Causal Learning Algorithms
[article]
2022
arXiv
pre-print
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The survey seeks to bring to the forefront the urgency of developing publicly available benchmarks and consensus-building standards for causal learning evaluation with observational data. ...
., based on statistical methods) to causal learning with big data (i.e., the intersection of causal inference and machine learning), in this survey, we review commonly-used datasets, evaluation methods ...
The views, opinions, and/or findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Army Research Office or the U.S. Government. ...
arXiv:2202.02896v1
fatcat:ykvg7gfwxfawjgkenvmmkbzpxa
Offline A/B Testing for Recommender Systems
2018
Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining - WSDM '18
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2018 pre-print arXiv:1801.07030v1 fatcat:s4v7cjaiubechgmoqs45sgx2he
We focus on evaluation methods that compute an estimator of the potential uplift in revenue that could generate this new technology. ...
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. ...
Doubly robust estimator. The easiest case is when we dispose of external knowledge like a reward model. We can use this as a control variate to improve our current estimator [6] . ...
doi:10.1145/3159652.3159687
dblp:conf/wsdm/GilotteCNAD18
fatcat:xynkxdlocbf5lmns56dmkksumm
CATE meets ML
2021
Digital Finance
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As it turns out, machine learning methods are the tool for generalized prediction models. ...
The presented toolbox of methods contains meta-learners, like the doubly-robust, R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized ...
Acknowledgements Financial support of the European Union's Horizon 2020 research and innovation ...
doi:10.1007/s42521-021-00033-7
fatcat:koynjpnt5bhvdd3ohnlwn6uvbu
Religiosity and parental educational aspirations for children in Kenya
2021
World Development Perspectives
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) and elicit parental aspirations for children using vignettes. ...
By employing inverse probability weighting with regression adjustment and multivalued treatment effects estimators on cross-sectional data, we show that membership in a religious institution and high levels ...
In Section 4, the data and measurement of variables and the empirical methods in this paper. ...
doi:10.1016/j.wdp.2021.100349
fatcat:5wzchliqkndqbfuvrkdtw5n5be
Page-level Optimization of e-Commerce Item Recommendations
[article]
2021
arXiv
pre-print
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Item recommendation modules on the IDP are often curated and statically configured for all customers, ignoring opportunities for personalization. ...
In our online A/B test, our framework improved click-through rate by 2.48% and purchase-through rate by 7.34% over a static configuration. ...
To overcome this issue, we adopt both Doubly Robust (DR) Estimator and Direct Method (DM) which evaluate both metrics in an unbiased manner [14] . ...
arXiv:2108.05891v1
fatcat:pthcwahr5vfsbflvanmtwy6rxy
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
2021
Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
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of competition and ad blockers for instance). ...
We propose a new approach for the estimation of the IPE that takes advantage of observed compliance information to prevent signal fading. ...
In that specific low-dimensional case, there is therefore no need to implement more complex models (Figure 8 ) such as doubly-robust methods or tree/forest-based methods. • Oracle predicts the theoretical ...
doi:10.1145/3447548.3467343
fatcat:yg33lsoy75h3de3yobmzjbeg6q
CATE meets ML – The Conditional Average Treatment Effect and Machine Learning
[article]
2021
arXiv
pre-print
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As it turns out, machine learning methods are the tool for generalized prediction models. ...
The presented toolbox of methods contains meta-learners, like the Doubly-Robust, R-, T- and X-learner, and methods that are specially designed to estimate the CATE like the causal BART and the generalized ...
Including more ML methods could improve the prediction accuracy depending on the data generating process. Using two-step sample splitting with cross-fitting further improves the prediction. ...
arXiv:2104.09935v2
fatcat:452b7na2hjdazmth2pzskeuhui
Individual Treatment Prescription Effect Estimation in a Low Compliance Setting
[article]
2020
arXiv
pre-print
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of competition and ad blockers for instance). ...
We propose a new approach for the estimation of the IPE that takes advantage of observed compliance information to prevent signal fading. ...
In that specific low-dimensional case, there is therefore no need to implement more complex models (Figure 8 ) such as doubly-robust methods or tree/forest-based methods. • Oracle predicts the theoretical ...
arXiv:2008.03235v2
fatcat:sdtfacpxuzexvdk3izvk3ouk6q
Evaluating Treatment Prioritization Rules via Rank-Weighted Average Treatment Effects
[article]
2021
arXiv
pre-print
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Our definition of the RATE nests a number of existing metrics, including the Qini coefficient, and our analysis directly yields inference methods for these metrics. ...
There are a number of available methods that can be used for choosing whom to prioritize treatment, including ones based on treatment effect estimation, risk scoring, and hand-crafted rules. ...
The purpose of the data is to provide a benchmark for uplift modeling, and therefore, the results are not meant to be used in a particular application. ...
arXiv:2111.07966v1
fatcat:jtzfcqbpezgnvbv2egrlkfpov4
Reinforcement Learning in Practice: Opportunities and Challenges
[article]
2022
arXiv
pre-print
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Then we discuss challenges, in particular, 1) foundation, 2) representation, 3) reward, 4) exploration, 5) model, simulation, planning, and benchmarks, 6) off-policy/offline learning, 7) learning to learn ...
, computer systems, and science and engineering. ...
A predictive model is built on domain knowledge, real-world data, and high-fidelity simulators; a robust method accounts for worst-case scenarios and takes conservative actions, and an adaptive method ...
arXiv:2202.11296v2
fatcat:xdtsmme22rfpfn6rgfotcspnhy
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