Causality, Reasoning in Research, and Why Science is Hard
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Transcript of Causality, Reasoning in Research, and Why Science is Hard
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Causality, Reasoning in Research, and Why Science is Hard
Sources: D. Jensen. “Research Methods for Empirical Computer Science.”William M.K. Trochim. “Research Methods Knowledgebase”
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More on CausalityWhat is causality?
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What’s Important About Causality?Explanation
◦Association provides prediction, but not explanation
◦ Identifying causal mechanisms may uncover underlying reasons for relationships
Control◦Understanding causality allows us to
predict the effects of actions without performing them
◦Allows more efficient exploration of the space of possible solutions
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Conditions for Causal Inference
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Problems with Association
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Are Feathers Associated with Flight?
Do they have a casual relationship with the ability to fly?
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Related FallaciesCommon (Questionable) Cause Fallacy
◦ This fallacy has the following general structure: 1. A and B are regularly associated (but no third,
common cause is looked for). 2. Therefore A is the cause of B.
◦ Called “Confusing Cause and Effect” fallacy, if in fact, there is not common cause for A and B
Post Hoc Fallacy◦ A Post Hoc is a fallacy with the following form:
1. A occurs before B. 2. Therefore A is the cause of B.
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Eliminating Common Causes
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Control
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Randomization
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Modeling
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Reasoning Methodologies in Research
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Types of Reasoning in Research
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Deductive vs. Inductive MethodologiesDeductive
Inductive
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What is Abduction?
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Examples of Abductive ReasoningA Medical Diagnosis
◦Given a specific set of symptoms, what is the diagnosis that would best explain most of them?
Jury Deliberations in a Criminal Case◦ Jurors must consider whether the prosecution
or the defense has the best explanation to cover all of the evidence
◦No certainty about the verdict, since there may exist additional evidence that was not admitted in the case
◦ Jurors make the best guess based on what they know
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“… when you have eliminated the impossible, whatever remains, however improbable, must be the truth.”
- Sherlock Holmes
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Abductive Reasoning in ScienceAbduction selects from among the
hypotheses being considered, the one that best explains the evidence◦Note that this requires that we consider
multiple alternative hypotheses Abductive Reasoning is closely
related to the statistical method of Maximum Likelihood Estimation
Possible threats to validity◦Small hypothesis spaces◦Small amounts of evidence to explain
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Challenges in Abductive ReasoningCreating hypothesis spaces likely to
contain the “true” hypothesis◦Approach: create large hypothesis spaces
Knowing when more valid hypotheses are missing from the hypothesis space◦Approach: constantly evaluate and revise the
hypothesis space (multiple working hypotheses)
Creating good sets of evidence to explain◦Approach: seek diverse and independent
evidence with which to evaluate hypotheses
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Why use multiple working hypotheses?Objectivity: Helps to separate you from your
hypotheses; shift from personal investment in hypotheses to testing the hypotheses
Focus: Reinforces a focus in falsification rather than confirmation
Efficiency: Allows experiments to be designed to distinguish among competing hypotheses rather than evaluating a single one
Harmony: Limits the potential for professional conflict and rejection because all hypotheses are considered and evaluated
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“Strong Inference” John R. Platt, Science, October 1964
◦ “Strong Inference - Certain systematic methods of scientific thinking may produce much more rapid progress than others.”
Not all science/research is created equalDon’t confuse research activity with effective research
◦ Activity: building systems; proving theorems; conducting surveys; writing and publishing articles; giving talks; obtaining grants
◦ Research: improved predictions; better understanding of relationships; improved control of computational artifacts
◦ Many researchers are active; only a subset do effective research
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Initial Questions for “Strong Inference”
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Arguments and FallaciesAside from general reasoning methodologies, one
must ensure the validity of all arguments used in any research endeavor
An argument ◦ Consists of one or more premises and a conclusion ◦ A premise is a statement (a sentence that is either true or
false) that is offered in support of the claim being made, which is the conclusion (also a sentence that is either true or false)
◦ Modus Ponens (and Modus Tollens)A fallacy
◦ Generally, an error in reasoning (differs from a factual error),
◦ An "argument" in which a logically invalid inference is made (deductive) or the premises given for the conclusion do not provide the needed degree of support. (inductive)
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Common Fallacies Ad Hominem Appeal to Authority Appeal to Belief Appeal to Common Practice Appeal to Popularity Begging the Question Biased Sample Hasty Generalization Ignoring A Common Cause Burden of Proof Straw Man
See:◦ http://en.wikipedia.org/wiki/Fallacy◦ http://www.nizkor.org/features/fallacies/
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Why is Science Hard?
[Notes]
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