"Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system."
Process of determining the cause and effect of historical events and processes by examining the relationships between different factors and analyzing their impact on outcomes.
Correlation vs. Causation: Understanding the difference between variables that are related and those that cause each other.
Types of Causal Relationships: Focusing on direct, inverse, indirect and spurious causal relationships.
Variables: The factors that determine a specific situation and its outcomes.
Statistical methods: Analyzing data and determining the significance of causal relationships.
Control Variables: Variables that behave in a consistent pattern, and so are not considered part of the causal relationship under investigation.
Time Series Analysis: The technique that studies past data in order to forecast future outcomes.
Snapshots vs. Focused studies: Studies that analyze everything happening at a specific moment vs. studies that focus on only one aspect at a time.
Historical Experiments: Conducting experiments on historical instances in order to make predictions on future situations.
Case Studies: In-depth studies of a specific event or context, in order to draw conclusions and learn from it.
Ethnography: Deep research into non-causal factors that create an incident or event.
Direct causation: A cause-and-effect relationship that is immediately evident and directly leads to an event.
Indirect causation: A cause-and-effect relationship that is not immediately apparent, but still leads to an event.
Contributory causation: A cause-and-effect relationship where multiple contributing factors combine and lead to an event.
Necessary causation: A cause-and-effect relationship where the cause is necessary for the effect to occur.
Sufficient causation: A cause-and-effect relationship where the cause is sufficient for the effect to occur.
Conditional causation: A cause-and-effect relationship where the cause only leads to the effect under specific conditions.
Counterfactual causation: A cause-and-effect relationship that relies on hypothetical scenarios and alternative possibilities.
Emergent causation: A cause-and-effect relationship that arises from complex systems or patterns of behavior.
Circular causation: A cause-and-effect relationship where the cause and effect are mutually reinforcing and continuously influence each other.
"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."