<?xml version='1.0' encoding='UTF-8' ?><rss version='2.0'><channel><title>Statistica Sinica: Volume 36, Online Special Issue I, April 2026</title><description>This is an example of an RSS feed</description><link>https://www3.stat.sinica.edu.tw/statistica/</link><lastBuildDate>Tue, 7 April 2026 00:01:00 +0000 </lastBuildDate><pubDate>Tue, 7 April 2026 00:01:00 +0000 </pubDate><ttl>1800</ttl>
<item>
<link>/statistica/J36N21/J36N2101/j36N2101.html</link>
<title> CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS </title>
<author>Razieh Nabi, Rohit Bhattacharya, Ilya Shpitser and James M. Robins </author>
<page> Nabi, Rohit Bhattacharya, Ilya Shpitser and James M. Robins (2026). CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS. Vol. 36 Online Special Issue, 1043-1081. DOI:10.5705/ss.202023.0382</page>
<description>&lt;span style='font-size=12pt;'&gt;&lt;center&gt;Abstract&lt;/center&gt; It is often said that the fundamental problem of causal inference is amissing data problem-the comparison of responses to two hypothetical treatment assignments is made difficult because for every experimental unit only one potential response is observed. In this paper, we consider the implications of the converse view: that missing data problems are a form of causal inference. We make explicit how the missing data problem of recovering the complete data law from the observed law can be viewed as identification of a joint distribution over counterfactual variables corresponding to values had we (possibly contrary to fact) been able to observe them. Drawing analogies with causal inference, we show how identification assumptions in missing data can be encoded in terms of graphical models defined over counterfactual and observed variables. We review recent results in missing data identification from this viewpoint. In doing so, we note interesting similarities and differences between missing data and causal identification theories. &lt;p&gt;Key words and phrases: Causal graphs, causal inference, missing not at random.&lt;/span&gt;</description>
</item>
<item>
<link>/statistica/J36N21/J36N2102/j36N2102.html</link>
<title> DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS" </title>
<author> Alexander W. Levis and Edward H. Kennedy </author>
<page>nder W. Levis and Edward H. Kennedy (2026). DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS". Vol. 36 Onlnie Special I, 1083-1095. DOI:10.5705/ss.202025.0210</page>
</item>
<item>
<link>/statistica/J36N21/J36N2103/j36N2103.html</link>
<title> DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS" </title>
<author> Shanshan Luo and Zhi Geng </author>
<page>han Luo and Zhi Geng (2026). DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS". Vol. 36 Onlnie Special I, 1097-1102. DOI:10.5705/ss.202025.0152</page>
</item>
<item>
<link>/statistica/J36N21/J36N2104/j36N2104.html</link>
<title> DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS" </title>
<author> Zeyi Wang and Mark J. van der Laan </author>
<page>Wang and Mark J. van der Laan (2026). DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS". Vol. 36 Onlnie Special I, 1103-1108. DOI:10.5705/ss.202025.0164</page>
</item>
<item>
<link>/statistica/J36N21/J36N2105/j36N2105.html</link>
<title> DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS" </title>
<author> Shu Yang and Jae Kwang Kim </author>
<page>ang and Jae Kwang Kim (2026). DISCUSSION ON "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS". Vol. 36 Onlnie Special I, 1109-1113. DOI:10.5705/ss.202025.0165</page>
</item>
<item>
<link>/statistica/J36N21/J36N2106/j36N2106.html</link>
<title> RESPONSE TO DISCUSSIONS OF "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS" </title>
<author>Alfredo Alegría and Xavier Emery </author>
<page>o Alegría and Xavier Emery (2026). RESPONSE TO DISCUSSIONS OF "CAUSAL AND COUNTERFACTUAL VIEWS OF MISSING DATA MODELS". Vol. 36 Online Special Issue I, 1115-1126. DOI:10.5705/ss.202025.0393</page>
</item>
</channel>
</rss>
