from description to causation
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Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
From Descriptionto Causation
Department of GovernmentLondon School of Economics and Political Science
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
1 Correlation vs. Causation
2 Over-Time Changes
3 Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
What makessomething a cause?
Write for 1 minute.
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
1 Correlation vs. Causation
2 Over-Time Changes
3 Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation
Correlation is the non-independence of twovariables for a set of observations
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation
Synonyms: correlation, covariation,relationship, association
Any correlation is potentially causalX might cause YY might cause XX and Y might be caused by ZX and Y might cause ZThere may be no causal relationship
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Flashback!
Two Categories of Inference:
1 Descriptive InferenceWhat are the facts?
2 Causal InferenceWhy does something occur?
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation is Causation?
The mind tends to interpretcorrelations and patterns as evidence
of causal relationships!
But this is rarely correct!
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: Wikimedia Commons
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: Wikimedia Commons
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: The Economist, 8 July 2016
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: The Economist, 8 July 2016
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: Randal Olson
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: Ministry of Justice, “Statistics on Race and theCriminal Justice System 2010”
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: StackExchange
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Source: TylerVigen.com
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Naive Causal InferenceCorrelations are not necessarily causal
Our mind thinks they are becausehumans are not very good at the kind ofcausal inference problems that socialscientists care about
Instead, we’re good at understandingphysical causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Physical causalityAction and reaction
Example:Picture a ball resting on top of a hillWhat happens if I push the ball?
Features:ObservableSingle-caseDeterministicMonocausal
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Physical causalityAction and reaction
Example:Picture a ball resting on top of a hillWhat happens if I push the ball?
Features:ObservableSingle-caseDeterministicMonocausal
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Physical causalityAction and reaction
Example:Picture a ball resting on top of a hillWhat happens if I push the ball?
Features:ObservableSingle-caseDeterministicMonocausal
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
1 Correlation vs. Causation
2 Over-Time Changes
3 Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Pre-Post Change HeuristicOur intuition about causation relies tooheavily on simple comparisons ofpre-post change in outcomes before andafter something happens
No change: no causationIncrease in outcome: positive effectDecrease in outcome: negative effect
Why is this flawed?
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Pre-Post Change HeuristicOur intuition about causation relies tooheavily on simple comparisons ofpre-post change in outcomes before andafter something happens
No change: no causationIncrease in outcome: positive effectDecrease in outcome: negative effect
Why is this flawed?
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Threats to Validity
Campbell and Ross talk about six “threats tovalidity” (i.e., threats to causal inference)
related to time-series analysis
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Flaws in causal inferencefrom pre-post comparisons
1 Maturation or trends2 Regression to the mean3 Selection4 Simultaneous historical changes5 Instrumentation changes6 Monitoring changes behaviour
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Maturation or trends
Is a shift in an outcome before and aftera policy change the impact of the policyor a small part of a longer time trend?
Case Study: Connecticut crackdown onspeeding (1955)
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Regression to the mean
Is a shift in an outcome before and aftera policy change the impact of the policyor a function of statistical variation?
Case Study: Connecticut crackdown onspeeding (1955)
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Selection
Is a shift in an outcome before and aftera policy the impact of the policy or theresult of the policy being implementedwhen outcomes are extreme?
Case Study: Connecticut crackdown onspeeding (1955)
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Simultaneous changes
Is the shift in an outcome before andafter a policy the impact of the policy orthe result of a simultaneous historicalshift?
Case Study: US Great Depression Policy
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Instrumentation changes
Is the shift in an outcome before andafter a policy the impact of the policy ora change in how the outcome ismeasured?
Case Study: Age-adjusted mortalityrates
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Monitoring changesbehaviour
Is the shift in an outcome before andafter a policy the impact of the policy ora change in response to measuring theoutcome per se?
Case Study: Educational testing
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
The more any quantitative socialindicator is used for socialdecision-making, the more subjectit will be to corruption pressuresand the more apt it will be todistort and corrupt the socialprocesses it is intended to monitor.
– Donald T. Campbell (1979)
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Flaws in causal inferencefrom pre-post comparisons
1 Maturation or trends2 Regression to the mean3 Selection4 Simultaneous historical changes5 Instrumentation changes6 Monitoring changes behaviour
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
1 Correlation vs. Causation
2 Over-Time Changes
3 Getting Systematic about Causality
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Directed Acyclic Graphs
Causal graphs (DAGs) provide a visualrepresentation of (possible) causal relationships
Causality flows between variables, which arerepresented as “nodes”
Variables are causally linked by arrowsCausality only flows forward in time
Nodes opening a “backdoor path” fromX → Y are confounds
“Selection bias” or “Confounding”
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Directed Acyclic Graphs
Causal graphs (DAGs) provide a visualrepresentation of (possible) causal relationships
Causality flows between variables, which arerepresented as “nodes”
Variables are causally linked by arrowsCausality only flows forward in time
Nodes opening a “backdoor path” fromX → Y are confounds
“Selection bias” or “Confounding”
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Directed Acyclic Graphs
Causal graphs (DAGs) provide a visualrepresentation of (possible) causal relationships
Causality flows between variables, which arerepresented as “nodes”
Variables are causally linked by arrowsCausality only flows forward in time
Nodes opening a “backdoor path” fromX → Y are confounds
“Selection bias” or “Confounding”
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Smoking Cancer
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Smoking Cancer
Age
Environment
GeneticPredisposition
ParentalSmoking
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Smoking Cancer
Age
Environment
GeneticPredisposition
ParentalSmoking
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Smoking Cancer
Age
Environment
GeneticPredisposition
ParentalSmoking
Correlation vs. Causation Over-Time Changes Getting Systematic about Causality
Causal InferenceCausal inference (typically) involves
gathering data in a systematic fashion inorder to assess the size and form of
correlation between nodes X and Y in such away that there are no backdoor paths
between X and Y by controlling for (i.e.,conditioning on, holding constant) any
confounding variables, Z.