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→因果效应度量指标的因果基元表示
P(e|c) - P(e|C\backslash c)
P(e|c) - P(e|C\backslash c)
</math>
</math>
|<math>
|<math>suff(e,c)+nec(e,c)-1</math>
suff + nec - 1
</math>
|<ref name="pearl_causality" /><ref>Eells, E. (1991). ''Probabilistic causality'' (Vol. 1). Cambridge University Press.</ref>
|<ref name="pearl_causality" /><ref>Eells, E. (1991). ''Probabilistic causality'' (Vol. 1). Cambridge University Press.</ref>
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|-
|Cheng度量
|Cheng度量
|<math>
|<math>suff(e,c)-nec^{\dagger}(e)</math>
|<math>\frac{suff(e,c)+nec(e,c)-1}{nec(e,c)}</math>
</math>
|<math>
\frac{suff + nec - 1}{nec}
</math>
|<ref name="pearl_causality" /><ref>Cheng, P. W., & Novick, L. R. (1991). Causes versus enabling conditions. ''Cognition'', ''40''(1-2), 83-120.</ref>
|<ref name="pearl_causality" /><ref>Cheng, P. W., & Novick, L. R. (1991). Causes versus enabling conditions. ''Cognition'', ''40''(1-2), 83-120.</ref>
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|Judea Pearl度量
|Judea Pearl度量
|<math>\mathrm{PNS}=P(e\mid c)-P(e\mid C\backslash c)</math>
|<math>\mathrm{PNS}（充要概率）=P(e\mid c)-P(e\mid C\backslash c)</math>
<math>\mathrm{PN}=\frac{P(e\mid c)-P(e\mid C\backslash c)}{P(e\mid c)}</math>
<math>\mathrm{PN}（必要概率）=\frac{P(e\mid c)-P(e\mid C\backslash c)}{P(e\mid c)}</math>
<math>\mathrm{PS}=\frac{P(e\mid c)-P(e\mid C\backslash c)}{1-P(e\mid C\backslash c)}</math>
<math>\mathrm{PS}（充分概率）=\frac{P(e\mid c)-P(e\mid C\backslash c)}{1-P(e\mid C\backslash c)}</math>
|<math>\mathrm{PNS}=suff(e,c)+nec(e,c)-1</math>
|<math>\mathrm{PNS}=suff(e,c)+nec(e,c)-1</math>
<math>\mathrm{PN}=\frac{suff(e,c)+nec(e,c)-1}{suff(e,c)}</math>
<math>\mathrm{PN}=\frac{suff(e,c)+nec(e,c)-1}{suff(e,c)}</math>