The doctrine of asatkaryavada affirms that the effect does not inhere in the cause, but is a new arising. See Nyaya for some details of the theory of causation in the Nyaya school. In Brahma Samhita, Brahma describes Krishna as the prime cause of all causes.Bhagavad-gītā 18.14 identifies five causes for any action (knowing which it can be perfected): the body, the individual soul, the senses, the efforts and the supersoul. According to Monier-Williams, in the Nyāya causation theory from Sutra I.2.I,2 in the Vaisheshika philosophy, from causal non-existence is effectual non-existence; but, not effectual non-existence from causal non-existence. A cause precedes an effect. With a threads and cloth metaphors, three causes are:

Co-inherence cause: resulting from substantial contact, substantial causes, threads are substantial to cloth, corresponding to Aristotles material cause. Non-substantial cause: Methods putting threads into cloth, corresponding to Aristotles formal cause. Instrumental cause: Tools to make the cloth, corresponding to Aristotles efficient cause.Monier-Williams also proposed that Aristotles and the Nyayas causality are considered conditional aggregates necessary to mans productive work. Buddhist philosophy
Karma is the causality principle focusing on 1) causes, 2) actions, 3) effects, where it is the minds phenomena that guide the actions that the actor performs. Buddhism trains the actors actions for continued and uncontrived virtuous outcomes aimed at reducing suffering. This follows the Subject–verb–object structure.The general or universal definition of pratityasamutpada (or "dependent origination" or "dependent arising" or "interdependent co-arising") is that everything arises in dependence upon multiple causes and conditions; nothing exists as a singular, independent entity.
Third, the principle that effects cannot precede causes can be invoked, by including on the right side of the regression only variables that precede in time the dependent variable; this principle is invoked, for example, in testing for Granger causality and in its multivariate analog, vector autoregression, both of which control for lagged values of the dependent variable while testing for causal effects of lagged independent variables. Regression analysis controls for other relevant variables by including them as regressors (explanatory variables). This helps to avoid false inferences of causality due to the presence of a third, underlying, variable that influences both the potentially causative variable and the potentially caused variable: its effect on the potentially caused variable is captured by directly including it in the regression, so that effect will not be picked up as an indirect effect through the potentially causative variable of interest. Given the above procedures, coincidental (as opposed to causal) correlation can be probabilistically rejected if data samples are large and if regression results pass cross-validation tests showing that the correlations hold even for data that were not used in the regression. Asserting with certitude that a common-cause is absent and the regression represents the true causal structure is in principle impossible.Apart from constructing statistical models of observational and experimental data, economists use axiomatic (mathematical) models to infer and represent causal mechanisms. Highly abstract theoretical models that isolate and idealize one mechanism dominate microeconomics. In macroeconomics, economists use broad mathematical models that are calibrated on historical data.