"Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system."
The process of establishing that one event caused another event.
Counterfactuals: Counterfactuals are hypothetical scenarios that imagine what would have happened if a certain event had not occurred or a certain treatment had not been implemented. Counterfactuals are essential in causal inference because they allow us to compare what happened in the actual world to what would have happened in a hypothetical world.
Types of causality: There are three types of causality: deterministic causality, probabilistic causality, and causal interaction. Deterministic causality assumes that for every cause, there is a single effect, while probabilistic causality assumes that causes only result in effects with a certain probability. Causal interactions occur when two or more causes combined to produce an outcome.
Correlation vs. Causation: Correlation refers to a relationship between two variables, while causation refers to the relationship between cause and effect. Correlation does not imply causation because there may be other variables at play that influence the observed relationship.
Observational studies: Observational studies are studies in which researchers simply observe the world and take notes about what they see. Observational studies can be used to identify relationship between variables but are limited in their ability to establish causal relationships.
Experimental studies: Experimental studies are studies in which researchers randomly assign participants to different treatments or interventions. By randomly assigning participants, researchers can more confidently establish causal relationships between treatment and outcome.
Probability theory: Probability theory is the study of stochastic processes and their associated phenomena. Probability theory is important in causal inference because it helps us quantify uncertainty and make decisions based on imperfect information.
Endogeneity: Endogeneity occurs when there is a two-way causal relationship between two variables. Endogeneity can be a problem in causal inference because it can lead to biased estimates and incorrect conclusions.
Selection Bias: Selection bias occurs when the selection process for a sample is not random. Selection bias can lead to biased estimates and incorrect conclusions.
Confounding: Confounding occurs when a third variable (known as a confounder) is related to both the independent and dependent variables. Confounding can lead to biased estimates and incorrect conclusions.
Matching: Matching is a technique used in observational studies to make two groups of subjects more similar to each other. This technique is used to minimize the effects of confounding variables.
Propensity score: The propensity score is the probability of receiving a treatment based on a set of observed covariates. The propensity score is used in observational studies to adjust for confounding by creating matched groups based on the probability of receiving a treatment.
Instrumental Variables: Instrumental variables are variables that are related to the treatment but are unrelated to the outcome. Instrumental variables can be used to overcome endogeneity in observational studies.
Mediation and Moderation: Mediation occurs when a third variable (the mediator) explains the relationship between the independent and dependent variables. Moderation occurs when the relationship between the independent and dependent variables depends on a third variable (the moderator).
Regression Analysis: Regression analysis is a statistical technique used to model the relationship between a dependent variable and one or more independent variables. Regression analysis is often used in causal inference to estimate the effect of a treatment.
Structural Equation Modeling: Structural equation modeling is a statistical technique used to model the relationships between multiple variables. Structural equation modeling is often used in causal inference to estimate the effects of multiple treatments.
Bayesian Inference: Bayesian inference is a statistical technique used to estimate the probability of a hypothesis being true. Bayesian inference is often used in causal inference to estimate the probability of a causal relationship between two variables.
Machine Learning: Machine learning is a type of artificial intelligence that involves using algorithms to learn from data. Machine learning is often used in causal inference to identify causal relationships among many variables.
Experiments: A controlled experiment is a method that involves manipulating one or more variables in a controlled setting to determine the cause-and-effect relationship between these variables.
Difference-in-Differences: Difference-in-differences (DID) is a method of estimating the causal effects of a treatment or intervention by comparing the changes in the outcome of a group of individuals who have been exposed to the treatment with the changes in the outcome of a group of individuals who have not been exposed to the treatment.
Natural Experiments: Natural experiments occur when a policy, intervention, or other event creates a naturally occurring control group, allowing researchers to estimate the causal effect of the treatment.
Regression Discontinuity: Regression discontinuity (RD) is a method of estimating the causal effect of a treatment or intervention by exploiting the discontinuities in the assignment mechanism that often arise in social programs.
Instrumental Variables: An instrumental variable (IV) is a variable that is correlated with the treatment but is not directly correlated with the outcome, allowing researchers to estimate the causal effect of the treatment.
Propensity Score Matching: Propensity Score Matching (PSM) is a method of estimating the causal effect of a treatment or intervention by matching participants based on a propensity score, which is a measure of the likelihood of receiving the treatment.
Time-Series Analysis: Time-series analysis is a method of estimating the causal effect of a treatment or intervention by analyzing changes in a variable over time.
Panel Data Methods: Panel data methods are techniques used to analyze data sets that contain observations of the same individuals or entities over time, allowing researchers to estimate the causal effects of interventions.
Structural Equation Modeling: Structural equation modeling (SEM) is a statistical method that allows researchers to estimate and test hypothesized causal relationships between variables in a formal, structural framework.
Bayesian Causal Inference: Bayesian causal inference is a probabilistic approach to estimating the causal effects of an intervention or treatment by modeling the likelihood of the outcome given the treatment and other variables.
"The main difference between causal inference and inference of association is that causal inference analyzes the response of an effect variable when a cause of the effect variable is changed."
"The science of why things occur is called etiology, and can be described using the language of scientific causal notation."
"Causal inference is said to provide the evidence of causality theorized by causal reasoning."
"Causal inference is widely studied across all sciences."
"Several innovations in the development and implementation of methodology designed to determine causality have proliferated in recent decades."
"Causal inference remains especially difficult where experimentation is difficult or impossible, which is common throughout most sciences."
"The approaches to causal inference are broadly applicable across all types of scientific disciplines."
"Many methods of causal inference that were designed for certain disciplines have found use in other disciplines."
"This article outlines the basic process behind causal inference."
"This article...details some of the more conventional tests used across different disciplines."
"Causal inference is difficult to perform and there is significant debate amongst scientists about the proper way to determine causality."
"There remain concerns of misattribution by scientists of correlative results as causal."
"There remain concerns...of the usage of incorrect methodologies by scientists."
"There remain concerns...of deliberate manipulation by scientists of analytical results in order to obtain statistically significant estimates."
"Particular concern is raised in the use of regression models, especially linear regression models."
"Particular concern is raised in the use of regression models, especially linear regression models."
"Causal inference is difficult to perform..."
"...there remain concerns of misattribution...to obtain statistically significant estimates."
"Particular concern is raised in the use of regression models, especially linear regression models."