Causal Analysis in Theory and Practice

December 20, 2000

Is the do(x) operator universal?

Filed under: do-calculus — moderator @ 12:00 am

From Bill Shipley, Universite de Sherbrooke, (Quebec) CANADA:

In most experiments, the external manipulation consists of adding (or subtracting) some amount from X without removing pre-existing causes of X. For example, adding 5 kg/h of fertilizer to a field, adding 5 mg/l of insulin to subjects etc. Here, the pre-existing causes of the manipulated variable still exert effects but a new variable (M) is added.
… The problem that I see with the do(x) operator as a general operator of external manipulation is that it requires two things: (1) removing any pre-existing causes of x and (2) setting x to some value. This corresponds to some types of external manipulations, but not all (or even most) external manipulations. I would introduce an add(x=n) operator, meaning "add, external to the pre-existing causal process, an amount 'n' of x''. Graphically, this consists of augmenting the pre-existing causal graph with a new edge, namely M-n–>X. Algebraically, this would consist of adding a new term –n– as a cause of X.

1 Comment »

  1. In many cases, your "additive intervention" represents indeed the only way we can intervene on a variable X. In fact, the general notion of intervention (Causality, page 113) involves replacing the equation of X by any other equation that fits the circumstances, not necessarily a constant X = x.

    What you are proposing corresponds to replacing the old equation of X, x = f(paX) by a new equation: x = f(paX) + 1 This replacement is usually treated under the heading "instrumental variables", since it is equivalent to writing x = f(paX) + I (where I is an instrument) and varying I from 0 to 1.

    There are three points to notice: 1. The additive manipulation CAN be represented in the do( ) framework — we merely apply the do( ) operator to the instrument I, and not to X itself. This is a different kind of manipulation that needs to be distinguished from do(x) because, as you noticed, the effect on y would be different.

    2. Scientists working with instrumental variables (e.g., epidemiologists) are not satisfied with estimating the effect of the instrument on Y, but are trying hard to estimate the effect of X itself. The former is known as "the effect of intention to treat" the latter "the effect of treatment" (see Causality, page 261).

    3. Consider the loopy example where LISREL fails y = bx +e1 + I, x = ay + e2.  If we interpret "total effects" as the response of Y to a unit change of the instrument I, then LISREL's formula obtains: The effect of I on Y is b/(1-ab) However, if we adhere to the notion of "per unit change in X", as opposed to "per unit change in an instrument of X", we get back the do-formula. The effect of X on Y is b, not b/(1-ab), even though the manipulation is done through an instrument. In other words, we change I from 0 to 1 and observe the changes in X and in Y; if we divide the change in Y by the change in X, we get b, not b/(1-ab).

    To summarize: Yes, additive manipulation is sometimes useful to model, normally it is done through instrumental variables, and we still need to distinguish between the effect of the instrument and the effect of X. The former is not stable (Causality, page 261) the latter is. Lisrel's formula corresponds to the effect of an instrument, not to the effect of X.

    Best wishes,
    ========Judea Pearl

    Comment by judea — December 20, 2000 @ 12:00 pm

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