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003			MX-MdCICY
005			20250625162431.0
040			_cCICY
090			_aB-19483
245	1	0	_aPredictive models aren't for causal inference
490	0		_vEcology Letters, 25(8), p.1741-1745, 2022
520	3		_aEcologists often rely on observational data to understand causal relationships. Although observational causal inference methodologies exist, predictive techniques such as model selection based on information criterion (e.g. AIC)remains a common approach used to understand ecological relationships. However, predictive approaches are not appropriate for drawing causal conclusions. Here, we highlight the distinction between predictive and causal inference and show how predictive techniques can lead to biased causal estimates. Instead, we encourage ecologists to valid causal inference methods such as the backdoor criterion, a graphical rule that can be used to determine causal relationships across observational studies.
650	1	4	_aBACK-DOOR CRITERION
650	1	4	_aCAUSAL INFERENCE
650	1	4	_aDIRECTED ACYCLIC GRAPHS (DAGS)
650	1	4	_aMODEL SELECTION
650	1	4	_aPREDICTION
700	1	2	_aArif, S.
700	1	2	_aMacneil, A.
856	4	0	_uhttps://drive.google.com/file/d/1LG_sWNGm5_3RZ0pmuLpMCNHawG4zQ2jV/view?usp=drivesdk _zPara ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx
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