Causal Inference
Causal inference is the process of determining cause-and-effect relationships from data, going beyond mere correlation. AI systems increasingly use causal reasoning to make more robust and interpretable decisions.
Understanding Causal Inference
Causal inference goes beyond the correlations that standard machine learning models detect, seeking to understand the true cause-and-effect relationships that generate observed data. While a classification model might find that ice cream sales and drowning rates are correlated, causal inference correctly identifies that both are caused by hot weather rather than one causing the other. Frameworks like Judea Pearl's do-calculus provide mathematical foundations for reasoning about interventions and counterfactuals. Practical applications include understanding which medical treatments actually improve outcomes, determining the true impact of marketing campaigns, and identifying effective policy interventions. Bayesian networks provide one computational tool for causal modeling, while randomized controlled trials offer empirical grounding. As AI systems increasingly inform consequential decisions in healthcare, economics, and public policy, integrating causal reasoning into machine learning has become a major research frontier.
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Data Science
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A/B Testing
A/B testing is an experimental method that compares two versions of a model, prompt, or interface to determine which performs better. In AI, A/B testing helps evaluate model outputs, UI changes, and prompt strategies by measuring user engagement or accuracy.
Annotation
Annotation is the process of adding labels or metadata to raw data to create training datasets for supervised learning. Data annotation can involve labeling images, tagging text, or marking audio segments.
Benchmark
A benchmark is a standardized test or dataset used to evaluate and compare the performance of different AI models. Common benchmarks include MMLU, HumanEval, and ImageNet.
Cross-Validation
Cross-validation is a model evaluation technique that splits data into multiple folds, training and testing on different subsets in rotation. K-fold cross-validation provides more reliable performance estimates than a single train-test split.
Data Augmentation
Data augmentation is a technique that artificially increases training dataset size by creating modified versions of existing data. In computer vision, this includes rotations, flips, and color changes; in NLP, it includes paraphrasing and synonym replacement.
Data Drift
Data drift occurs when the statistical properties of production data change over time compared to the training data. Drift can degrade model performance and requires monitoring and retraining strategies to address.
Data Labeling
Data labeling is the process of assigning meaningful tags or annotations to raw data to create supervised learning datasets. High-quality labeled data is essential for training accurate machine learning models.
Dimensionality Reduction
Dimensionality reduction is the process of reducing the number of features in a dataset while preserving its essential structure. Techniques like PCA and t-SNE help with visualization, noise reduction, and computational efficiency.