|
Bad Control
In statistics, bad controls are variables that introduce an unintended discrepancy between regression coefficients and the effects that said coefficients are supposed to measure. These are contrasted with confounders which are "good controls" and need to be included to remove omitted variable bias. This issue arises when a bad control is an outcome variable (or similar to) in a causal model and thus adjusting for it would eliminate part of the desired causal path. In other words, bad controls might as well be dependent variables in the model under consideration. Angrist and Pischke (2008) additionally differentiate two types of bad controls: a simple bad-control scenario and proxy-control scenario where the included variable partially controls for omitted factors but is partially affected by the variable of interest. Pearl (1995) provides a graphical method for determining good controls using causality diagrams and the back-door criterion and front-door criterion. Examples ''Sim ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Confounder
In statistics, a confounder (also confounding variable, confounding factor, extraneous determinant or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations.Pearl, J., (2009). Simpson's Paradox, Confounding, and Collapsibility In ''Causality: Models, Reasoning and Inference'' (2nd ed.). New York : Cambridge University Press. The existence of confounders is an important quantitative explanation why correlation does not imply causation. Confounds are threats to internal validity. Definition Confounding is defined in terms of the data generating model. Let ''X'' be some independent variable, and ''Y'' some dependent variable. To estimate the effect of ''X'' on ''Y'', the statistician must suppress the effects of extraneous variables that influence both ''X'' and ''Y''. We say that ''X'' and ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Causal Diagram With A Mediator
Causality (also referred to as causation, or cause and effect) is influence by which one event, process, state, or object (''a'' ''cause'') contributes to the production of another event, process, state, or object (an ''effect'') where the cause is partly responsible for the effect, and the effect is partly dependent on the cause. In general, a process has many causes, which are also said to be ''causal factors'' for it, and all lie in its past. An effect can in turn be a cause of, or causal factor for, many other effects, which all lie in its future. Some writers have held that causality is metaphysically prior to notions of time and space. Causality is an abstraction that indicates how the world progresses. As such a basic concept, it is more apt as an explanation of other concepts of progression than as something to be explained by others more basic. The concept is like those of agency and efficacy. For this reason, a leap of intuition may be needed to grasp it. Accordingly ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
Omitted-variable Bias
In statistics, omitted-variable bias (OVB) occurs when a statistical model leaves out one or more relevant variables. The bias results in the model attributing the effect of the missing variables to those that were included. More specifically, OVB is the bias that appears in the estimates of parameters in a regression analysis, when the assumed specification is incorrect in that it omits an independent variable that is a determinant of the dependent variable and correlated with one or more of the included independent variables. In linear regression Intuition Suppose the true cause-and-effect relationship is given by: :y=a+bx+cz+u with parameters ''a, b, c'', dependent variable ''y'', independent variables ''x'' and ''z'', and error term ''u''. We wish to know the effect of ''x'' itself upon ''y'' (that is, we wish to obtain an estimate of ''b''). Two conditions must hold true for omitted-variable bias to exist in linear regression: * the omitted variable must be a determi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu] |
