Conditionals

Causality

Causality (also referred to as causation, or cause and effect) is influence by which one event, process, state, or object (a cause) contributes to the production of another event, process, state, or object (an effect) where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that indicates how the world progresses. As such a basic concept, it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it. Accordingly, causality is implicit in the logic and structure of ordinary language. In English studies of Aristotelian philosophy, the word "cause" is used as a specialized technical term, the translation of Aristotle's term αἰτία, by which Aristotle meant "explanation" or "answer to a 'why' question". Aristotle categorized the four types of answers as material, formal, efficient, and final "causes". In this case, the "cause" is the explanans for the explanandum, and failure to recognize that different kinds of "cause" are being considered can lead to futile debate. Of Aristotle's four explanatory modes, the one nearest to the concerns of the present article is the "efficient" one. David Hume, as part of his opposition to rationalism, argued that pure reason alone cannot prove the reality of efficient causality; instead, he appealed to custom and mental habit, observing that all human knowledge derives solely from experience. The topic of causality remains a staple in contemporary philosophy. (Wikipedia).

Causality
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Causal Inference Introduction

Causal Inference is a set of tools used to scientifically prove cause and effect, very commonly used in economics and medicine. This series will go over the basics that any data scientist should understand about causal inference - and point them to the tools they would need to perform it.

From playlist Causal Inference - The Science of Cause and Effect

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Instrumental Variables

We introduce Instrumental Variables

From playlist Causal Inference - The Science of Cause and Effect

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Violations of Exchangeability - Causal Inference

Today I talk about violations of exchangeability, e.g., common causes, confounding, selection bias.

From playlist Causal Inference - The Science of Cause and Effect

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Causation vs. Association - Causal Inference

In this video I talk about the difference between causation and association and explain each of these concepts through an example. Enjoy!

From playlist Causal Inference - The Science of Cause and Effect

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Confounding Example 2 - Causal Inference

Today I cover an example of an endogenous condition, a conditioned upon confounder (and collider) which is caused by the endogenous condition, and selection bias.

From playlist Causal Inference - The Science of Cause and Effect

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Fundamental Question - Causal Inference

In this video, I define the fundamental question and problem of causal inference and use an example to further explain the concept.

From playlist Causal Inference - The Science of Cause and Effect

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Observational Studies - Causal Inference

Today I talk about how observational studies are great examples of when causation does not equal association by visiting a real world example. The next videos will explore how we extract causal information from observational studies

From playlist Causal Inference - The Science of Cause and Effect

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D-Separation - Causal Inference

Today I talk about association in causal diagrams, e.g., D-separation. By applying the rules I outline in this video you will be able to determine if two variables are associated.

From playlist Causal Inference - The Science of Cause and Effect

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Ideal Experiment - Causal Inference

In this video, I give you more details about the fundamental question and the fundamental problem of causal inference with the help of an example (our ideal experiment).

From playlist Causal Inference - The Science of Cause and Effect

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Wolfram Physics I: Basic Formalism, Causal Invariance and Special Relativity

Find more information about the summer school here: https://education.wolfram.com/summer/school Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announcement post: http://wolfr.am/physics-announcement Find the tools to build a universe: https:

From playlist Wolfram Summer Programs

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No Cause for Concern: Indefinite Causal Ordering as a Tool for Understanding Entanglement

Understanding the sorts of explanations and inferences that causal processes countenance is of course of great interest to philosophers and physicists (among others).  But what can be said about physical processes that fail to exhibit classical causal structure?  Indefinite causal ordering

From playlist Franke Program in Science and the Humanities

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Causality: From Aristotle to Zebrafish - Frederick Eberhardt - 10/16/2019

Earnest C. Watson Lecture by Professor Frederick Eberhardt, "Causality: From Aristotle to Zebrafish." What causes what? If correlation does not equal causation, then how can we untangle the “why” behind processes that regulate the brain, the climate, or the economy? And how does this appl

From playlist Caltech Watson Lecture Series

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Wolfram Physics Project: A Discussion with Fay Dowker

Fay Dowker joins Stephen Wolfram, Jonathan Gorard and Max Piskunov for a Wolfram Physics Project discussion. Begins at 1:47 Originally livestreamed at: https://twitch.tv/stephen_wolfram Stay up-to-date on this project by visiting our website: http://wolfr.am/physics Check out the announc

From playlist Wolfram Physics Project Livestream Archive

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Causation from the Point of View of Physics, Jenann Ismael

There has been an enormous burgeoning of interest in causation across the sciences. One can open up a journal in microbiology and be assailed with detailed models of  the causal structure of cells and proteins. One can find textbooks on the increasing array computational tools for causal s

From playlist Franke Program in Science and the Humanities

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SDS 613: Causal Machine Learning — with Emre Kiciman

#CausalMachineLearning #CausalInference #DoWhyOpenSource Dr. Emre Kiciman, Senior Principal Researcher at Microsoft Research joins the podcast to share his world-leading knowledge on causal machine learning. This episode is brought to you by Datalore, https://datalore.online/SDS, the col

From playlist Super Data Science Podcast

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Statistical Rethinking 2023 - 06 - Good & Bad Controls

Course details: https://github.com/rmcelreath/stat_rethinking_2023 Intro music: https://www.youtube.com/watch?v=PDohhCaNf98 Outline 00:00 Introduction 01:43 Causal implications 14:28 do-calculus 16:59 Backdoor criterion 40:48 Pause 41:22 Good and bad controls 1:09:34 Summary 1:26:27 Bonu

From playlist Statistical Rethinking 2023

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Causal Inference - Frederick Eberhardt - 6/7/2019

Changing Directions & Changing the World: Celebrating the Carver Mead New Adventures Fund. June 7, 2019 in Beckman Institute Auditorium at Caltech. The symposium features technical talks from Carver Mead New Adventures Fund recipients, alumni, and Carver Mead himself! Since 2014, this Fun

From playlist Carver Mead New Adventures Fund Symposium

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Statistical Rethinking 2022 Lecture 06 - Good & Bad Controls

Slides and other course materials: https://github.com/rmcelreath/stat_rethinking_2022 Intro video: https://www.youtube.com/watch?v=6erBpdV-fi0 Intro music: https://www.youtube.com/watch?v=Pc0AhpjbV58 Chapters: 00:00 Introduction 01:23 Parent collider 08:13 DAG thinking 27:48 Backdoor cri

From playlist Statistical Rethinking 2022

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Causality and Entanglement in Holography - The Connected Wedge Theorem Revisited - Jonathan Sorce

IAS It from Qubit Workshop Workshop on Spacetime and Quantum Information Tuesday December 6, 2022 Wolfensohn Hall One puzzling aspect of holography is that it conjectures a duality between a physical theory with a single rigid causal structure (the non-gravitational "boundary theory") and

From playlist IAS It from Qubit Workshop - Workshop on Spacetime and Quantum December 6-7, 2022

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Correlation does not Imply Causality, but then again… (7-4)

Correlation Does Not Imply Causation. When we see a correlation, we should not assume a cause-and-effect relationship between the variables. Correlation does not mean one isn’t causing the other, either; we just need more information. The correlation between two variables may be caused by

From playlist Correlation And Regression in Statistics (WK 07 - QBA 237)

Related pages

Feedback | Causal inference | Anticausal system | Causal structure | Determinism | Path analysis (statistics) | Quantum Zeno effect | Dynamic stochastic general equilibrium | Causal Markov condition | Counterfactual conditional | Regression analysis | Causality | Correlation does not imply causation | Rubin causal model | Statistics | Bayesian network | Karl Popper | Randomness | Newcomb's paradox | Granger causality | Schrödinger's cat | Supply and demand | Butterfly effect | Cross-spectrum | Experiment | Causality conditions | Cross-sectional data | Thermodynamic operation | Causal loop diagram | Antecedent (logic) | Consequent | David Hume | Incompatibilism | Infinite regress | Minkowski space | Thermodynamic process | Arthur Danto | Linear regression | Ishikawa diagram | Chaos theory | Conditional probability | Vector autoregression | Causal filter | Indicative conditional | Probabilistic causation | Directed acyclic graph | Causality (book) | Correlation | Time series | Clinical trial | Cross-validation (statistics) | Causal model | Causal system | Arrow of time | Cross-correlation | Validity (statistics)