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Maximising the Adjustment Coefficient Form

Maximising the Adjustment Coefficient Form

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now that we've seen why randomized experiments are magic from a few different perspectives we'll move on to more general identification beyond the backdoor adjustment for the rest of this lecture and the first is the front door adjustment let's quickly recall the backdoor adjustment so if we're looking at p of y given t here we have non-causal association flowing along these backdoor paths but we can fix that by blocking those backdoor paths by just conditioning on w2 and c for example so we can get identification in this graph just using the backdoor adjustment but what about this graph where w is unobserved so we can't condition on w here we can't block that backdoor path can we still identify the causal effect of t on y if this is the causal graph and we haven't observed w it turns out that we can using the front door adjustment so in this graph we have causal association flowing along the directed path t to m to y and we have

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