= Jonas Peters
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<Jonas Peters> studies <causal inference> and the foundations of <conditional independence> testing. With <Rajen D. Shah>, he proved that a test with uniform finite-sample validity over an unrestricted continuous-data null class cannot obtain nontrivial power against alternatives in their setting.<The Hardness of Conditional Independence Testing and the Generalised Covariance Measure by Rajen D. Shah and Jonas Peters>[Impossibility result and its distributional scope]{ref}
Their constructive response is the <generalized covariance measure statistic>: regress each of two variables on the conditioning variables and examine the covariance of their residuals. Suitable regression-error rates and moment conditions yield asymptotic validity. Such a test targets conditional residual association; arbitrary forms of conditional dependence need not produce a nonzero residual covariance.<The Hardness of Conditional Independence Testing and the Generalised Covariance Measure by Rajen D. Shah and Jonas Peters>[Section3.1 and asymptotic conditions]{ref}
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