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curious_dan
curious_dan
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Requirements What are the requirements for causalperforming a counterfactual inference?

Background to question:

In statistics, machine learning, etc. there are algorithms for estimating the causal effect of an intervention X on an outcome Y.

In order to justify these estimates, they claim they have verified the following two assumptions.

  1. No unobserved confounders assumption (NUCA)
  2. Strong Ignorability/Positivity

The first is that all confounders Z that determine the assignment X, and that also determine the outcome Y, are in the model.

The second is that for all values z that a confounder Z takes on, the probability P(Z=z|X)>0. That is, the data has examples of all combinations of Z and X.

With these two assumptions satisfied, these algorithms can then adjust the estimate of the effect of X on Y according to the confounding effects of Z.

Question:

I do not know the source of the requirement of these two assumptions, but the source ought to be philosophical.

Can someone point me towards a review of philosophical literature that covers the requirements for inferring a counterfactual outcome?

Requirements for causal inference

Background to question:

In statistics, machine learning, etc. there are algorithms for estimating the causal effect of an intervention X on an outcome Y.

In order to justify these estimates, they claim they have verified the following two assumptions.

  1. No unobserved confounders assumption (NUCA)
  2. Strong Ignorability/Positivity

The first is that all confounders Z that determine the assignment X, and that also determine the outcome Y, are in the model.

The second is that for all values z that a confounder Z takes on, the probability P(Z=z|X)>0. That is, the data has examples of all combinations of Z and X.

With these two assumptions satisfied, these algorithms can then adjust the estimate of the effect of X on Y according to the confounding effects of Z.

Question:

I do not know the source of the requirement of these two assumptions, but the source ought to be philosophical.

Can someone point me towards a review of philosophical literature that covers the requirements for inferring a counterfactual outcome?

What are the requirements for performing a counterfactual inference?

Can someone point me towards a review of philosophical literature that covers the requirements for inferring a counterfactual outcome?

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curious_dan
curious_dan
  • 131
  • 4

Background to my question:

In statistics, machine learning, etc. there are algorithms for estimating the causal effect of an intervention X on an outcome Y.

In order to justify these estimates, they claim they have verified the following two assumptions.

  1. No unobserved confounders assumption (NUCA)
  2. Strong Ignorability/Positivity

The first is that all confounders Z that determine the assignment X, and that also determine the outcome Y, are in the model.

The second is that for all values z that a confounder Z takes on, the probability P(Z=z|X)>0. That is, the data has examples of all combinations of Z and X.

With these two assumptions satisfied, these algorithms can then adjust the estimate of the effect of X on Y according to the confounding effects of Z.

My questionQuestion:

I do not know the source of the requirement of these two assumptions, but the source ought to be philosophical.

Can someone point me towards a review of philosophical literature that covers the necessary requirements for inferring a counterfactual outcome?

Background to my question:

In statistics, machine learning, etc. there are algorithms for estimating the causal effect of an intervention X on an outcome Y.

In order to justify these estimates, they claim they have verified the following two assumptions.

  1. No unobserved confounders assumption (NUCA)
  2. Strong Ignorability/Positivity

The first is that all confounders Z that determine the assignment X, and that also determine the outcome Y, are in the model.

The second is that for all values z that a confounder Z takes on, the probability P(Z=z|X)>0. That is, the data has examples of all combinations of Z and X.

With these two assumptions satisfied, these algorithms can then adjust the estimate of the effect of X on Y according to the confounding effects of Z.

My question

I do not know the source of the requirement of these two assumptions, but the source ought to be philosophical.

Can someone point me towards a review of philosophical literature that covers the necessary requirements for inferring a counterfactual outcome?

Background to question:

In statistics, machine learning, etc. there are algorithms for estimating the causal effect of an intervention X on an outcome Y.

In order to justify these estimates, they claim they have verified the following two assumptions.

  1. No unobserved confounders assumption (NUCA)
  2. Strong Ignorability/Positivity

The first is that all confounders Z that determine the assignment X, and that also determine the outcome Y, are in the model.

The second is that for all values z that a confounder Z takes on, the probability P(Z=z|X)>0. That is, the data has examples of all combinations of Z and X.

With these two assumptions satisfied, these algorithms can then adjust the estimate of the effect of X on Y according to the confounding effects of Z.

Question:

I do not know the source of the requirement of these two assumptions, but the source ought to be philosophical.

Can someone point me towards a review of philosophical literature that covers the requirements for inferring a counterfactual outcome?

Source Link
curious_dan
curious_dan
  • 131
  • 4

Requirements for causal inference

Background to my question:

In statistics, machine learning, etc. there are algorithms for estimating the causal effect of an intervention X on an outcome Y.

In order to justify these estimates, they claim they have verified the following two assumptions.

  1. No unobserved confounders assumption (NUCA)
  2. Strong Ignorability/Positivity

The first is that all confounders Z that determine the assignment X, and that also determine the outcome Y, are in the model.

The second is that for all values z that a confounder Z takes on, the probability P(Z=z|X)>0. That is, the data has examples of all combinations of Z and X.

With these two assumptions satisfied, these algorithms can then adjust the estimate of the effect of X on Y according to the confounding effects of Z.

My question

I do not know the source of the requirement of these two assumptions, but the source ought to be philosophical.

Can someone point me towards a review of philosophical literature that covers the necessary requirements for inferring a counterfactual outcome?