This article explains how Tubi monitors the quality of its recommendation system by identifying potential failure points, tracking key data streams such as model input, final recommendation output, and training data, and designing a scalable, real‑time monitoring solution with clear protocols and extensible metrics.
Researchers from Alibaba Cloud AI and Zhejiang University present AMFormer, a Transformer‑based model that incorporates arithmetic feature interaction, demonstrating superior fine‑grained modeling, sample efficiency, and generalization on synthetic and real‑world tabular datasets, establishing a new state‑of‑the‑art in deep tabular learning.
This article walks readers through the evolution of artificial intelligence—from early expert systems and machine learning basics to convolutional neural networks, the AlphaGo series, MuZero's rule‑free learning, and the generative power of large language models like ChatGPT—highlighting how deep learning, Monte Carlo tree search, and self‑play collaborate to achieve unprecedented performance across games, science, and language.
Discover the essential Python libraries for data science in 2024, from versatile tools like Taipy and Pandas to powerful machine‑learning frameworks such as TensorFlow, PyTorch, and Scikit‑Learn, each with key features, use‑cases, and GitHub links to boost your analytics career.
This tutorial provides a visual, step‑by‑step guide to NumPy, covering array creation, arithmetic and broadcasting, indexing, aggregation, matrix operations, reshaping, and practical examples such as computing mean‑squared error for machine‑learning models, illustrated with code snippets and diagrams.
This article presents Ant Financial's practical approaches to anomaly detection and attribution diagnosis, detailing the underlying concepts, four methodological categories, specific algorithms such as VBEM, AnoSVGD and Autoformer, multi‑dimensional factor analysis, real‑world challenges, and operational benefits for KPI monitoring and incident response.
This article explains how applying machine learning to database operations—specifically AIOps for MySQL—can automate index recommendation by parsing SQL, extracting semantic and statistical features, generating candidate index combinations, and training an XGBoost model to predict optimal indexes, reducing reliance on manual DBA work.
This article presents OPPO's comprehensive advertising recall system, detailing the transition from the old to the new architecture with ANN support, the selection of main‑road recall models, the construction of offline evaluation metrics, sample optimization techniques, model enhancements, multi‑scenario training strategies, and outlook for future improvements.
This article explains how AIOps leverages AI, machine learning, and dynamic threshold techniques to handle massive, multimodal monitoring data, improve anomaly detection, and enhance IT operation reliability through metric classification, baseline prediction, and automated fault remediation.
This article introduces the three core machine learning categories—supervised, unsupervised, and reinforcement learning—detailing their definitions, typical algorithms, and real‑world applications, and then compares generative and discriminative models, highlighting key examples, characteristics, and use‑case differences.
This article provides an in‑depth overview of regression prediction, covering linear models like OLS, Lasso, Ridge, and Bayesian approaches, as well as non‑linear techniques such as tree ensembles, SVR, KNN, neural networks, and advanced deep learning frameworks for tabular data.
This article provides a comprehensive summary of ten regression algorithms—including linear, ridge, Lasso, decision tree, random forest, gradient boosting, SVR, XGBoost, LightGBM, and neural network regression—detailing their principles, advantages, disadvantages, suitable scenarios, and offering core Python code examples for each.
This article details the design, integration, and operational practices of a comprehensive user and item profiling tag system, covering tag taxonomy, construction methods, update cycles, access strategies, algorithmic implementations, and real‑world applications such as marketing, attribution analysis, and A/B testing.
Researchers from Carnegie Mellon University and the University of Bonn have released the open‑source A‑SOiD platform, which learns and predicts user‑defined behaviors solely from video, offering transparent, bias‑aware AI that can be applied to animal studies, human actions, and diverse pattern‑recognition domains.
This article explains how Kubernetes supports GPU workloads for AI and machine learning, covering device plugins, pod GPU requests, oversubscription, security isolation, cloud‑provider node setup, and protecting GPU nodes from non‑GPU pods.
This article explains how to detect and handle outliers in datasets using PHP and machine‑learning techniques, covering Z‑Score and Isolation Forest algorithms as well as methods to delete or replace anomalous values to improve data quality and model accuracy.
This article explains how a high‑throughput, low‑latency flight search system handles billions of daily requests by employing multi‑level caching, distributed Redis and MongoDB stores, AI‑driven TTL optimization, and advanced load‑balancing techniques such as pooling and overload protection.
This beginner-friendly guide walks through loading the classic Iris dataset, performing exploratory data analysis, and implementing four fundamental classifiers—Decision Tree, Logistic Regression, Support Vector Machine, and K‑Nearest Neighbors—complete with training, visualization, and accuracy evaluation, illustrating a full machine‑learning workflow.
This article explores how Laravel can serve as a flexible backend platform for integrating artificial intelligence and machine learning technologies—such as predictive analytics, chatbots, image/video analysis, and recommendation systems—by presenting practical code examples, discussing opportunities, challenges, and best‑practice tools.
This article explains how to build a comprehensive user‑portrait feature system for credit business, covering business goals, data collection, labeling, modeling workflow, technical challenges, multi‑source fusion, deployment, evaluation, management, practical applications, and future extensions using AI and big‑data techniques.
This article presents Fliggy Travel's end‑to‑end algorithmic solutions for hotel category planning, introduces the LINet group‑recommendation model that incorporates location and travel intent, and details the PETS two‑stage model for selecting hot‑sale hotels under recall constraints, together with experimental results and practical insights.
The article details JD’s smart supply‑chain innovations, describing an explainable AI forecasting method that boosts prediction accuracy while maintaining interpretability, and an end‑to‑end inventory management model based on multi‑quantile RNNs that improves replenishment decisions, reduces costs, and enhances overall operational efficiency.
This article compiles a detailed list of interview question topics from leading tech firms—including search, algorithm engineering, NLP, multimodal LLMs, advertising, recommendation, risk control, and big‑data domains—covering algorithms, system design, machine‑learning concepts, and practical coding challenges.
This article introduces causal learning, explains its distinction from traditional correlation‑based machine learning, outlines its three main parts, discusses the two primary paradigms—learning with known causal graphs and learning via causal discovery—and highlights their advantages, challenges, and recent research directions.
Bilibili combats personal‑attack and trolling comments by combining sector‑specific keyword databases, user‑group analysis, advanced word‑matching (including pinyin and homophone detection) and multiple NLP/graph models, which has cut personal‑attack reports in entertainment, film and gaming by about 32 % and trolling reports by roughly 25 % between June and December 2023.
This article presents Ant Group's research on causal debiasing for recommendation and marketing, covering the background of bias, common bias types, causal graph analysis, two correction approaches—data‑fusion based MDI and back‑door adjustment based DMBR—along with experimental results on public and proprietary datasets and real‑world deployment insights.
This article presents how causal inference methods are applied to Kuaishou’s single‑column short‑video recommendation, covering the platform’s recommendation scenario, model representations, duration bias mitigation, viewing‑time prediction techniques such as D2Q and TPM, experimental results, and future research directions.
This paper presents a novel framework that leverages negative samples during large language model distillation through three stages—Negative Assistive Training, Negative Calibration Enhancement, and Adaptive Self‑Consistency—demonstrating significant accuracy gains on challenging mathematical reasoning benchmarks and improved generalization to out‑of‑distribution tasks.
This article compiles a categorized list of recent model papers, covering change models and various prediction models—including time series, machine learning, gray prediction, and deep learning—providing direct references for students and researchers interested in AI and data‑driven modeling.
This curated list gathers recent articles on statistical and probabilistic models, covering clustering analysis, various linear regression techniques, and causal analysis, providing convenient links for students and researchers to explore each topic in depth.
This article explains the role of A/B experiments in recommendation systems, outlines their workflow, shares practical tips and parameter design strategies, and demonstrates how to use experiment parameters and feature flags for efficient testing, optimization, and full‑scale deployment.
This article details Meituan's two‑year exploration of AIOps for incident management, covering the challenges of massive, real‑time operational data, the AI‑driven modules for risk prevention, fault detection, diagnosis, and similar‑incident recommendation, and future directions such as intelligent log detection and change recognition.
This article presents how Ant Group applies causal inference techniques—including confounding bias analysis, double‑difference methods, DiDTree, and shrinkage‑based causal trees—to correct biases in risk‑control scenarios, detailing the theoretical background, algorithmic designs, experimental validation, and practical deployment.
This article introduces ten data‑mining applications for marketing—including customer segmentation, purchase forecasting, market‑basket analysis, churn prediction, sentiment analysis, response modeling, recommendation systems, brand reputation, competitive analysis, and public‑opinion monitoring—each illustrated with concise Python code examples.
This article presents a detailed overview of world models and their role in reinforcement learning, explains how causal inference can enhance model-based RL, discusses sample efficiency challenges, and shares experimental findings and practical insights from recent research and industry applications.
This article presents ten practical Python code examples that demonstrate common data analysis techniques—such as handling missing values, sorting, pivot tables, visualization, association rules, outlier detection, time‑series forecasting, clustering, feature selection, and cross‑validation—to help improve business decision effectiveness.
This article introduces ten common statistical techniques used in data analysis—including descriptive statistics, correlation, t‑test, ANOVA, linear regression, PCA, outlier detection, frequency distribution, time‑series analysis, and non‑parametric tests—providing concise explanations and Python code snippets for each method.
This article introduces the use of graph models in Baidu's recommendation system, covering graph fundamentals, common graph algorithms such as graph embedding and graph neural networks, the evolution of the Feed graph model, and its subsequent promotion across multiple product lines.
This article explains the back‑propagation algorithm in neural networks, starting with a simple single‑neuron example using ReLU, Sigmoid and MSE, then extending to multi‑layer matrix‑based networks, providing detailed Python code, gradient calculations, and comparisons with TensorFlow implementations.
The article explains how the data team adopted the open‑source machine‑learning platform MindsDB to simplify data integration, enable SQL‑based model training and inference, manage model versions, and dramatically shorten recommendation system development cycles, achieving up to 30% efficiency gains.
This article provides a comprehensive introduction to popular Python libraries used in artificial intelligence, such as NumPy, OpenCV, scikit-image, Pillow, SimpleCV, Mahotas, Ilastik, Scikit-learn, SciPy, NLTK, spaCy, LibROSA, Pandas, Matplotlib, Seaborn, Orange, PyBrain, Theano, Keras, Caffe, MXNet, PaddlePaddle, CNTK, and more, including code snippets and usage examples.
This article presents Meituan's two‑year exploration of AIOps in incident management, detailing risk‑prevention change detection, real‑time anomaly discovery, automated root‑cause diagnosis, multi‑dimensional KPI analysis, and similar‑event recommendation, while sharing architectural designs, algorithmic techniques, performance results, and future directions.
This article presents Baidu's comprehensive approach to feed recommendation ranking, covering business and data background, feature engineering principles, core algorithmic strategies, system architecture design, and upcoming plans to integrate large language models for more intelligent and fair recommendations.
This article presents Ant Group's causal debiasing techniques for recommendation and marketing, covering bias background, data‑fusion based MDI model, back‑door adjustment methods, experimental results on public and industry datasets, and practical applications in advertising and e‑commerce.
OPPO revamped its advertising recall system by replacing a latency‑prone directional pipeline with an ANN‑based full‑ad personalized architecture, employing a dual‑tower LTR model, multi‑path auxiliary branches, refined offline metrics, price‑sensitive and hard‑negative sampling, and hybrid joint training, which together boosted ARPU by about 15%.
This article presents OPPO's advertising recall system, detailing the transition from the legacy architecture to a new ANN‑based design, model selection criteria, offline evaluation metrics, sample optimization techniques, and various model improvements that together achieved significant ARPU gains.
This article introduces a machine‑learning approach for correlating text and image data, covering preprocessing, feature extraction, model training, experimental results, and future directions, and provides complete Python code examples using NLP and deep‑learning libraries.
Streamlit is a free, open‑source Python framework that lets machine‑learning engineers quickly turn scripts into interactive web apps, featuring top‑to‑bottom script execution, widget‑as‑variable handling, caching, GPU support, and seamless integration with tools like Git.
The article summarizes Ant Group's twenty accepted NeurIPS 2023 papers, covering advances in generative AI, time‑series forecasting, 3D image synthesis, and other machine‑learning topics, and provides brief overviews of three highlighted works along with links to the remaining studies.
This article examines recommendation system problem definitions, differences between academia and industry, offline evaluation pitfalls and data leakage issues, data construction challenges with datasets like MovieLens, and evaluates whether large language models can serve as effective solutions for modern recommendation tasks.
This article reviews the growing interest in causal learning within machine learning, explaining what causal learning is, its advantages over purely correlational methods, and detailing two main paradigms—learning with known causal structures and learning via causal discovery—along with examples, challenges, and future directions.
This article introduces the architecture and core functions of Automotive Home Push Platform, covering its development history, technical implementation, monitoring system, and future plans for intelligent message distribution.
This article presents a comprehensive collection of Python code snippets demonstrating essential data cleaning methods—including handling missing values, outlier detection, type conversion, formatting, duplicate removal, normalization, one‑hot encoding, text preprocessing, and dataset merging—providing practical guidance for preparing data for analysis or machine‑learning tasks.
This article introduces fundamental probability notation, explains the difference between discriminative and generative models, and provides a comprehensive overview of autoencoders and variational autoencoders, covering their architectures, loss functions, latent spaces, and practical applications in image manipulation.
The article examines how OPPO tackles the challenges of sparse, multi‑scenario app‑distribution data by deploying a unified modeling framework, leveraging MMoe and oCPX techniques to enhance recommendation performance, reduce costs, and preserve user value across its software store and game center.
Taobao’s Technical Industry Data team designed a four‑level VOC tagging hierarchy to unify fragmented user‑feedback sources, evaluated label similarity with vector‑based distance matrices, optimized tag groups via entropy‑driven re‑grouping, built a stacking ensemble of FastText and TextCNN achieving over 90% accuracy, and deployed an automated production pipeline that generates tags, maintains ODPS tables, and provides APIs for rapid experimentation.
This article introduces ten popular Python libraries for computer vision, describing their main features, typical applications, and providing concise code examples to help beginners and practitioners quickly choose and use the right tools for image processing and deep learning tasks.
The article traces Alibaba’s Alimama platform from classic control‑based bidding through linear programming and reinforcement‑learning approaches to generative‑AI‑driven strategies, detailing how deep‑learning models, offline and sustainable online RL frameworks, and large‑language‑model‑based bidding reshape automated auctions, fairness, and scalability in e‑commerce advertising.
This article introduces Alibaba's Alink platform and its online learning capabilities, discusses common challenges in machine‑learning pipelines, explains Alink’s algorithm‑to‑application connection, various computation modes, usage methods, and details the evolution from Model Flow 1.0 to the more versatile Model Flow 2.0, including pipeline integration, incremental training, and embedding prediction services.
A forum discussing frontiers in short video recommendation algorithms, featuring academic research from Kuaishou and collaborations with universities, including topics like reinforcement learning and graph neural networks for personalized recommendations.
The HC² framework enhances multi‑scenario ad ranking by jointly applying a generalized contrastive loss on shared representations and an individual contrastive loss on scenario‑specific layers, using label‑aware positive sampling, diffusion‑noise negative sampling, and inverse‑similarity weighting, achieving consistent offline gains and up to 2.5% CVR and 3.7% GMV improvements in Alibaba’s live system.
This article explains what intelligent operations (AIOps) are, outlines a four‑layer platform architecture, and showcases real‑world practices such as load‑balancing link repair, MySQL container self‑healing, composite service tracing, component‑based orchestration, and AI‑driven log analysis, concluding with future prospects.
Kapacity V0.2 introduces an AI‑powered, traffic‑driven replica prediction algorithm for cloud‑native autoscaling, featuring a Linear‑Residual model, a lightweight Swish Net time‑series forecaster, custom metric support, and open‑source tools, aiming to improve resource efficiency and reduce operational risk.
This article outlines the challenges of multi-task and multi-scenario modeling for large-scale C-end services, reviews key industry approaches such as Shared-Bottom, MMoE, PLE, ESMM, and LHUC, and details Zhaozhuan’s own EPNET-based solution that improved click-through and conversion rates.
This article presents a comprehensive overview of using instrumental variables for causal inference and causal generalization in machine learning, discussing deep learning limitations, Pearl's causal hierarchy, two‑stage regression, challenges with unobserved confounders, automatic IV generation, and applications in economics and social networks.
This article examines the challenges of freight‑peak periods, reviews industry surge‑pricing tactics, and presents a comprehensive dynamic‑pricing framework—including data collection, supply‑demand analysis, price adjustment, real‑time monitoring, and optimization models—to improve service quality, reduce disputes, and maximize platform revenue.
This article explains how to build and apply a data‑driven metric system, covering end‑to‑end design principles, business‑ versus data‑driven approaches, frameworks such as OSM, GSM and HEART, statistical and machine‑learning techniques, causal inference, and practical case studies that illustrate alerting, diagnosis, and strategy deployment in product operations.
This article reviews animal recognition technology, covering its background, basic principles, image‑processing, feature extraction, machine‑learning and deep‑learning methods, dataset construction, preprocessing, and feature‑selection techniques, and provides Python code examples for implementing CNNs and traditional classifiers.
The article presents a comprehensive pricing solution for an e‑commerce platform that combines Bayesian statistical modeling, MCMC sampling, and causal inference (including Dragonnet) to achieve controllable, fine‑grained SKU‑level price estimation and optimization.
The article announces that Alibaba Cloud’s AI platform PAI and ECNU researchers’ paper on the Optimal Linear Subspace Search (OLSS) algorithm was selected for CIKM 2023, explains how OLSS accelerates diffusion‑model sampling by operating in higher‑dimensional linear subspaces, and provides details of the paper and its visual results.
The CNCC 2023 Technical Forum highlighted how large models can boost recommendation systems with stronger generalization and knowledge understanding, while also raising challenges like high computational costs, interpretability, and ethics, featuring talks from experts at Xiaohongshu, USTC, Tsinghua, Renmin University, and Huawei.
This article describes the design and implementation of a Document Analytics & Anti‑Fraud Support platform for Hong Kong virtual banking, detailing its OCR/NLP‑driven pipeline, dynamic rule engine, multi‑template PDF processing, model training, and the resulting improvements in fraud detection and operational efficiency.
This article introduces ten widely used machine learning algorithms for data prediction, explains their core concepts, and provides complete Python code snippets using scikit‑learn and related libraries to help readers implement regression, classification, and time‑series forecasting tasks.
This article presents a comprehensive design for Zhuanzhuan's home‑page recommendation pipeline, detailing the system architecture, challenges of traffic efficiency and diversity, and a two‑stage solution that applies Proximal Policy Optimization reinforcement learning in the re‑ranking module and Determinantal Point Process optimization in the coarse‑ranking and traffic‑pool stages, followed by offline simulation, online deployment, and evaluation metrics.
The DataFunSummit2023 online conference brings together experts from Tencent and Kuaishou to present cutting‑edge research on causal inference for large‑scale A/B testing, including deep‑learning‑based multi‑experiment effect estimation, a distributed causal inference framework (Fast‑Causal‑Inference), and strategies for evaluating long‑term policy impacts.
The article demystifies Stable Diffusion by explaining its low‑cost latent‑space design and conditioning mechanisms, comparing it to autoregressive, VAE, flow‑based and GAN models, detailing the iterative noise‑to‑image process, token‑based text‑to‑image control, version differences, common generation issues, and providing implementation code examples.
This article introduces ten popular Python libraries—Augmentor, imgaug, albumentations, nlpaug, textaugment, pytorch‑geometric, audiomentations, nlpaugment, keras‑augment, and OpenCV—that provide powerful image, text, audio, and graph data augmentation techniques to improve model generalization and robustness.
This article explains how graph models are applied in Baidu's recommendation system, covering graph basics, common graph algorithms such as graph embeddings and GNNs, and the evolution and deployment of the Feed graph model across multiple product lines.
This article presents Kuaishou's approach to solving the content cold-start problem by analyzing its impact on video growth, detailing the challenges of sparse and biased training data, and describing a suite of graph‑neural‑network, I2U/U2I, TDM, and debiasing techniques that improve early video exposure and long‑term ecosystem health.
This article presents practical feature‑engineering methods for ten common business domains, explaining the purpose of each feature, the extraction technique, and providing ready‑to‑run Python code snippets to help build more accurate predictive models.
This article presents a comprehensive overview of multi‑task and multi‑scenario algorithms applied to recommendation systems, covering background challenges, algorithm taxonomy, recent research, detailed model architectures such as TAML, CausalInt and DFFM, experimental results on public and private datasets, and a Q&A discussion.
Hugging Face has released Candle, a Rust‑written machine‑learning framework aimed at serverless inference, offering lightweight binaries, GPU support, and performance gains over Python‑based PyTorch, while sparking debate over Rust’s learning curve and the future of AI deployment.
This article examines the fairness of recommendation systems from both consumer and provider viewpoints, discussing sources of bias, definitions of equality and equity, measurement metrics such as CGF and MMF, and proposes causal embedding models to mitigate unfairness while ensuring sustainable system performance.
This article presents a comprehensive case study of NIO's Power swap‑station ecosystem, detailing the business context, key forecasting challenges, the evolution from classical statistical models to deep‑learning architectures with specialized embeddings, and the practical outcomes and future plans for improving prediction accuracy.
In this talk, senior researcher Dai Quanyu from Huawei Noah's Ark Lab presents his work on applying causal inference to identify and correct various biases in recommendation systems, detailing underlying theoretical frameworks, bias‑mitigation algorithms such as inverse propensity weighting and robust learning, and real‑world product deployments.
Stephen Wolfram explains the inner workings of ChatGPT, covering its transformer architecture, probability‑based word selection, training on massive text corpora, the role of embeddings, neural network layers, attention mechanisms, and the challenges of modeling language, offering a deep technical overview for AI enthusiasts.
This comprehensive guide demystifies over one hundred core mathematical modeling terms—from basic concepts like models and abstraction to advanced topics such as optimization, dynamic systems, stochastic processes, statistical methods, and machine learning—helping newcomers confidently navigate the field.
The inaugural AFAC2023 Financial Intelligence Challenge, co‑organized by the China Computer Federation and Ant Group, attracted over 4,700 teams, showcased cutting‑edge AI solutions for finance such as market opinion generation, compliance detection, and pet‑age recognition, and culminated in the public launch of the Fin‑Eval benchmark dataset for financial large‑model evaluation.
This article details how AutoHome's homepage pop‑up leverages precise targeting, extensive feature engineering, and multi‑stage DeepFM‑based models with attention and LHUC modules to accurately identify car‑buying users, improve vehicle‑series recommendations, and achieve a 355% conversion rate increase.
This presentation explores the challenges of label scarcity and class imbalance in graph‑based risk‑control scenarios, proposing semantic‑aware active learning and prototype‑driven sampling strategies to improve node classification performance on imbalanced graph datasets.
This article introduces five free artificial‑intelligence coding assistants, explains how they accelerate and secure software development, and addresses common concerns about AI’s impact on programmers and job security.
From Turing’s 1950 imitation game to GPT‑4’s multimodal vision‑language capabilities, the field has evolved from simple rule‑based programs like ELIZA and PARRY, through statistical learning and the 2017 Transformer breakthrough, to large-scale generative models that achieve fluent conversation yet still grapple with hallucination and true understanding.
This article explains the difference between machine learning and deep learning, compares ML algorithms with traditional logic code, introduces the scikit‑learn library, demonstrates data preprocessing, model training with RandomForestRegressor, and shows how to build a voting regressor for disease progression prediction using Python.
This article introduces ten common anomaly detection approaches—including statistical thresholds, boxplots, clustering, isolation forest, LOF, collaborative filtering, robust covariance, NLP, computer‑vision, and time‑series methods—each accompanied by concise Python code snippets illustrating how to identify outliers in different data domains.
This article explains the concept of information entropy, its mathematical definition, how to compute it for simple events like coin flips and dice rolls, and demonstrates its role in decision‑tree learning by calculating information gain for a watermelon dataset.
This article explains the inner workings of ChatGPT, covering how large language models predict the next token using probability distributions, the role of embeddings, the transformer architecture with attention heads, training methods, loss functions, and why such a massive neural network can produce coherent, human‑like language.
This article introduces the CDSBen model, a machine‑learning‑based benchmark that translates real database transaction patterns into storage‑layer I/O workloads, enabling precise, isolated performance testing of the cloud‑native veDB storage system.
The paper introduces Score‑Weighted VCG, a learning‑based ad auction framework that models externalities by learning a monotone scoring function and solving a weighted‑welfare matching problem, achieving incentive compatibility, individual rationality, and near‑optimal revenue and welfare on synthetic and large‑scale Taobao data.
This article presents a comprehensive approach to estimating causal effects on clustered data using a double‑difference method, introduces the DiConfounder algorithm built on Rubin Causal Model extensions, details data characteristics, model assumptions, six‑step pipeline, and reports competitive results on the ACIC2022 challenge.
This article introduces the fundamentals of causal inference from observational data, explains confounding and the SUTVA assumptions, presents the do‑operator, and details four de‑confounding strategies—including RCT‑based resampling, feature‑decomposition, double machine learning, and back‑/front‑door adjustments—followed by real‑world applications in recommendation systems and resource allocation.
