A Crash Course in Causality: Inferring Causal Effects from Observational Data

Start Date: 07/05/2020

Course Type: Common Course

Course Link: https://www.coursera.org/learn/crash-course-in-causality

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About Course

We have all heard the phrase “correlation does not equal causation.” What, then, does equal causation? This course aims to answer that question and more! Over a period of 5 weeks, you will learn how causal effects are defined, what assumptions about your data and models are necessary, and how to implement and interpret some popular statistical methods. Learners will have the opportunity to apply these methods to example data in R (free statistical software environment). At the end of the course, learners should be able to: 1. Define causal effects using potential outcomes 2. Describe the difference between association and causation 3. Express assumptions with causal graphs 4. Implement several types of causal inference methods (e.g. matching, instrumental variables, inverse probability of treatment weighting) 5. Identify which causal assumptions are necessary for each type of statistical method So join us.... and discover for yourself why modern statistical methods for estimating causal effects are indispensable in so many fields of study!

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Course Introduction

A Crash Course in Causality: Inferring Causal Effects from Observational Data In this course we look at how causal effects work and how to use them in machine learning. We will look at two types of causal effects: weighted and unweighted. We will use both statistical and non-statistical methods to analyze many different kinds of causal effects. We will also look at the concept of correlation and how causal effects are evaluated. This leads us to consider the question: what is causal uncertainty? We’ll also look at how to assess for causal uncertainty using the different methods discussed in the course. We will then apply these concepts to a case study of the topic from the Department of Energy & Environmental Science. After completing this course you will be able to: - Utilize the statistical techniques discussed in introduction to machine learning to assess for causal uncertainty in machine learning models. - Apply random variables in machine learning models to assess for causal uncertainty in machine learning models. - Construct machine learning models and apply them to different types of causal effects. - Assess for causal uncertainty using the different methods discussed in the course. This course was created by the University of Edinburgh and Edinburgh Business School, under the leadership of Professor Tim Maudlin. A number of contributions have been made by researchers from the Universities of Leeds and Leeds, including by Edinburgh Business School students. This course is part of the EIT-Digital consortium, part of the EIT-Digital project (https://www.eit-digital.eu/

Course Tag

Instrumental Variable Propensity Score Matching Causal Inference Causality

Related Wiki Topic

Article Example
Causality For nonexperimental data, causal direction can often be inferred if information about time is available. This is because (according to many, though not all, theories) causes must precede their effects temporally. This can be determined by statistical time series models, for instance, or with a statistical test based on the idea of Granger causality, or by direct experimental manipulation. The use of temporal data can permit statistical tests of a pre-existing theory of causal direction. For instance, our degree of confidence in the direction and nature of causality is much greater when supported by cross-correlations, ARIMA models, or cross-spectral analysis using vector time series data than by cross-sectional data.
Granger causality Granger also stressed that some studies using "Granger causality" testing in areas outside economics reached "ridiculous" conclusions. "Of course, many ridiculous papers appeared", he said in his Nobel lecture. However, it remains a popular method for causality analysis in time series due to its computational simplicity. The original definition of Granger causality does not account for latent confounding effects and does not capture instantaneous and non-linear causal relationships, though several extensions have been proposed to address these issues.
Causality These theorists claim that the important concept for understanding causality is not causal relationships or causal interactions, but rather identifying causal processes. The former notions can then be defined in terms of causal processes.
Causal patch A causal patch is a region of spacetime connected within the relativistic framework of causality (causal light cones).
Causality Whereas David Hume argued that causes are inferred from non-causal observations, Immanuel Kant claimed that people have innate assumptions about causes. Within psychology, Patricia Cheng (1997) attempted to reconcile the Humean and Kantian views. According to her power PC theory, people filter observations of events through a basic belief that causes have the power to generate (or prevent) their effects, thereby inferring specific cause-effect relations.
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Causal system The causality of systems also plays an important role in digital signal processing, where filters are constructed so that they are causal, sometimes by altering a non-causal formulation to remove the lack of causality so that it is realizable. For more information, see causal filter.
Granger causality A method for Granger causality has been developed that is not sensitive to deviations from the assumption that the error term is normally distributed. This method is especially useful in financial economics, since many financial variables are non-normally distributed. Recently, asymmetric causality testing has been suggested in the literature in order to separate the causal impact of positive changes from the negative ones.
Causal reasoning Causal reasoning is the process of identifying causality: the relationship between a cause and its effect. The study of causality extends from ancient philosophy to contemporary neuropsychology; assumptions about the nature of causality may be shown to be functions of a previous event preceding a later one. The first known protoscientific study of cause and effect occurred in Aristotle's "Physics".
Causality (book) Causality: Models, Reasoning and Inference is a book by Judea Pearl. It is an exposition and analysis of causality. It is considered to have been instrumental in laying the foundations of the modern debate on causal inference in several fields including statistics, computer science and epidemiology. In this book, Pearl espouses the Structural Causal Model (SCM) that uses structural equation modeling. This model is a competing viewpoint to the Rubin causal model.
Causality Causality has the properties of antecedence and contiguity. These are topological, and are ingredients for space-time geometry. As developed by Alfred Robb, these properties allow the derivation of the notions of time and space. Max Jammer writes "the Einstein postulate ... opens the way to a straightforward construction of the causal topology ... of Minkowski space." Causal efficacy propagates no faster than light. Thus, the notion of causality is metaphysically prior to the notions of time and space.
Causal fermion system This notion of causality fits together with the "causality" of the above causal action in the sense that if two space-time points formula_52 are space-like separated, then the Lagrangian formula_62 vanishes. This corresponds to the physical notion of causality that spatially separated space-time points do not interact. This causal structure is the reason for the notion "causal" in causal fermion system and causal action.
Causality While derivations in causal calculus rely on the structure of the causal graph, parts of the causal structure can, under certain assumptions, be learned from statistical data. The basic idea goes back to Sewall Wright's 1921 work on path analysis. A "recovery" algorithm was developed by Rebane and Pearl (1987) which rests on Wright's distinction between the three possible types of causal substructures allowed in a directed acyclic graph (DAG):
Causal system Classically, nature or physical reality has been considered to be a causal system. Physics involving special relativity or general relativity require more careful definitions of causality, as described elaborately in causality (physics).
Causality Causality is one of the most fundamental and essential notions of physics. Causal efficacy cannot propagate faster than light. Otherwise, reference coordinate systems could be constructed (using the Lorentz transform of special relativity) in which an observer would see an effect precede its cause (i.e. the postulate of causality would be violated).
Causality Causality (also referred to as causation, or cause and effect) is the agency or efficacy that connects one process (the "cause") with another process or state (the "effect"), where the first is understood to be partly responsible for the second, and the second is dependent on the first. In general, a process has many causes, which are said to be causal factors for it, and all lie in its past. An effect can in turn be a cause of many other effects. Although retrocausality is sometimes referred to in thought experiments and hypothetical analyses, causality is generally accepted to be temporally bound so that causes always precede their dependent effects (although in some contexts such as economics they may coincide in time; see Instrumental variable for how this is dealt with econometrically).
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Causality conditions There is a hierarchy of causality conditions, each one of which is strictly stronger than the previous. This is sometimes called the causal ladder. The conditions, from weakest to strongest, are: