Genres

Causal Inference


Causal Inference Books

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Causal Inference: The ...
 
by
Scott Cunningham
Causal Inference in Statistics: A Primer
The Book of Why: The New Science of Cause and Effect
Observation and Experiment: An Introduction to Causal Inference
Causal Inference: What If
Mostly Harmless Econometrics: An Empiricist's Companion
Elements of Causal Inference: Foundations and Learning Algorithms (Adaptive Computation and Machine Learning series)
Causality: Models, Reasoning, and Inference
Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
Mastering 'Metrics: The Path from Cause to Effect
The Effect
Causal Inference in Python: Applying Causal Inference in the Tech Industry
Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)
Causal Inference and Discovery in Python: Unlock the secrets of modern causal machine learning with DoWhy, EconML, PyTorch and more
Causal Inference for the Brave and True
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Quotes Tagged “Causal Inference”

Tom Golway
“ To me AI becomes interesting when it has awareness of causality, not just correlation. When it can deal with ambiguity and that sensitive dependencies exist outside the bounds of a defined data set which can lead to different outcomes. - Tom Golway ”
― Tom Golway
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“ Pearl combines aspects of structural equations models and path diagrams. In this approach, assumptions underlying causal statements are coded as missing links in the path diagrams. Mathematical methods are then used to infer, from these path diagrams, which causal effects can be inferred from the data, and which cannot. Pearl's work is interesting, and many researchers find his arguments that path diagrams are a natural and convenient way to express assumptions about causal structures appealing. ...more ”
― Guido W. Imbens, Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
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