# Problem 2.4.1 part a) from Pearl et al. "Causal Inference in Statistics: A Primer"

I am solving exercise 2.4.1 part a) from Pearl et al. "Causal Inference in Statistics: A Primer" (2016).

I have found that in Figure 2.9, variables $$Y$$ and $$Z_1$$ are independent conditional on variables $$\{X, Z_2, Z_3\}$$: $$Y \perp \!\!\! \perp Z_1 | \{X, Z_2, Z_3\}.$$ (The same answer is found in the Solution Manual.) I want to illustrate this empirically, so I generate data that is compatible with the graph as follows (in R):

n=1e5
set.seed(1); Z1=rnorm(n)
set.seed(2); Z2=rnorm(n)
set.seed(3); Z3=rnorm(n)+Z1+Z2
set.seed(4); X=rnorm(n)+Z1+Z3
set.seed(5); W=rnorm(n)+X
set.seed(6); Y=rnorm(n)+W+Z1+Z2


I then estimate a model $$Y=\beta_0+\beta_1 Z_1+\beta_2 Z_2+\beta_3 Z_3+\beta_4 X+\varepsilon$$ and expect to find that $$\hat\beta_1$$ is not statistically significant because of the conditional independence mentioned above. However, the result is out of line:

> m1=lm(Y~Z1+Z2+Z3+X)
> summary(m1)

Call:
lm(formula = Y ~ Z1 + Z2 + Z3 + X)

Residuals:
Min      1Q  Median      3Q     Max
-5.7134 -0.9562 -0.0052  0.9533  6.7408

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -0.009183   0.004500  -2.041   0.0413 *
Z1           0.993558   0.007770 127.868   <2e-16 ***
Z2           1.002707   0.006349 157.923   <2e-16 ***
Z3          -0.009440   0.006354  -1.486   0.1373
X            1.008032   0.004507 223.636   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 1.423 on 99995 degrees of freedom
Multiple R-squared:  0.8817,    Adjusted R-squared:  0.8817
F-statistic: 1.863e+05 on 4 and 99995 DF,  p-value: < 2.2e-16


This could of course be an unlucky case. I have tried a few other random seeds for generating data, but I am consistently getting a highly significant $$\hat\beta_1$$. ($$\hat\beta_3$$ becomes significant in many other cases, as I think it should be.)

What am I doing wrong?

By the way, I have assessed conditional independence between several other pairs of variables in Figure 2.9, and there I am getting the expected results from the same simulated data (just different regressions).

• Hey, Richard! I'm working my way through the same book. I'm up to Study question 4.3.1, and finally hit a fairly hard roadblock. If you have any ideas, I'd love to have them! It's my question on the Counterfactual Expectation Calculation. Apr 2 '20 at 14:32
• @AdrianKeister, I have not got that far in the book yet myself. Apr 2 '20 at 14:58

I think your code for simulating the data has a typo. In the line beginning with set.seed(6) $$Z_1$$ should be $$Z_3$$.