refactor: move files and add white bg

Author: sgratzl

content/blog/2021-01-15-causal-effect-mobility.Rmd

@@ -159,7 +159,7 @@ age, increasing $A$ would *decrease* $Y$. In this case, age is a
 confounder and the $g$-formula would correctly recover the negative
 relationship. For causal inference, this is the correct conclusion.
 
-![](/blog/images/causal-simple-confounder.svg)
+![](/blog/2021-01-15-causal-effect-mobility_files/causal-simple-confounder.svg)
 
 Things get trickier
 when there are time varying variables.
@@ -505,7 +505,7 @@ stay-at-home decreases deaths, as we would expect.
 that these results are preliminary.)
 
 
-![](/blog/images/causal-effect-mobility-bigstates.svg)
+![](/blog/2021-01-15-causal-effect-mobility_files/causal-effect-mobility-bigstates.svg)
 
 ## What's Next?
 

content/blog/2021-01-15-causal-effect-mobility.html

@@ -150,7 +150,7 @@ <h2>Causal Inference</h2>
 age, increasing <span class="math inline">\(A\)</span> would <em>decrease</em> <span class="math inline">\(Y\)</span>. In this case, age is a
 confounder and the <span class="math inline">\(g\)</span>-formula would correctly recover the negative
 relationship. For causal inference, this is the correct conclusion.</p>
-<p><img src="/blog/images/causal-simple-confounder.svg" /></p>
+<p><img src="/blog/2021-01-15-causal-effect-mobility_files/causal-simple-confounder.svg" /></p>
 <p>Things get trickier
 when there are time varying variables.
 Consider weekly mobility and death data
@@ -463,7 +463,7 @@ <h2>The Data and the Results</h2>
 stay-at-home decreases deaths, as we would expect.
 (We remind the reader
 that these results are preliminary.)</p>
-<p><img src="/blog/images/causal-effect-mobility-bigstates.svg" /></p>
+<p><img src="/blog/2021-01-15-causal-effect-mobility_files/causal-effect-mobility-bigstates.svg" /></p>
 </div>
 <div id="whats-next" class="section level2">
 <h2>What’s Next?</h2>