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Yapay ZekâUzmanlıkAIML-CAU-007

Heterogeneous Effects and Machine Learning for Causal Estimation

Using flexible ML models to estimate effects that vary by individual, with orthogonalisation to avoid bias.

Bu konunun henüz Türkçe çevirisi yok, bu yüzden İngilizce gösteriliyor.

Zorluk
5 / 5
Önem
3 / 5
Çalışma süresi
1–2 sa
Çalışma türü
Karma

Neden önemli

The learner can explain how double machine learning removes regularisation bias and interpret a conditional average treatment effect.

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Rota AIML-CAU-007
Doğal Sayılar What Is a Model? Değişkenler ve Sabitler Learning from Data Tasks: Regression, Classification, and Beyond Features and Targets Korelasyon ve Nedensellik Datasets and Splits Classification Framing Linear Regression I: Prediction and Residuals Prediction versus Causation Scores and Logits Decision Trees Loss and Fitting: Squared Error Potential Outcomes and Treatment Effects Sigmoid and Probabilities Doğrusal Regresyon II: En Küçük Kareler Uydurma Overfitting and Underfitting Logistic Regression Propensity Scores and Inverse Probability Weighting Gradient Boosting Heterogeneous Effects and Machine Learning for Causal Estimation