Directed Acyclic Graphs – Paul Hünermund, Ph.D.

Mapping Unchartered Territory

A frequent point of criticism against Directed Acyclic Graphs is that writing them down for a real-world problem can be a difficult task. There are numerous possible variables to consider and it’s not clear how we can determine all the causal relationships between them. We recently had a Twitter discussion where exactly this argument popped up again.

I don't think there is such a thing as the "ground truth" in this context. In any context there are an enormous number of variables that could conceivably be included in a DAG, and which DAG you write will depend on whether you start with a tDAG or not. You said you disagreed.

— Jason Abaluck (@Jabaluck) September 4, 2019

Continue reading Mapping Unchartered Territory

PO vs. DAGs – Comments on Guido Imbens’ New Paper

Guido Imbens published a new working paper in which he develops a detailed comparison of the potential outcomes framework (PO) and directed acyclic graphs (DAG) for causal inference in econometrics. I really appreciate this paper, because it introduces a broader audience in economics to DAGs and highlights the complementarity of both approaches for applied econometric work. Continue reading PO vs. DAGs – Comments on Guido Imbens’ New Paper

Graphs and Occam’s Razor

One argument / point of criticism I often hear from people who start exploring Directed Acyclic Graphs (DAG) is that graphical models can quickly become very complex. When you read about the methodology for the first time you get walked through all these toy models – small, well-behaved examples with nice properties, in which causal inference works like a charm.

You Can’t Test Instrument Validity

Instrumental variable (IV) estimation is an important technique in causal inference and applied empirical work. Continue reading You Can’t Test Instrument Validity

The Origins of Graphical Causal Models

Here is an interesting bit of intellectual history. In his 2000 book “Causality”, Judea Pearl describes how he got to the initial idea that sparked the development of causal inference based on directed acyclic graphs. Continue reading The Origins of Graphical Causal Models

No Free Lunch in Causal Inference

Last week I was teaching about graphical models of causation at a summer school in Montenegro. You can find my slides and accompanying R code in the teaching section of this page. It was lots of fun and I got great feedback from students. After the workshop we had stimulating discussions about the usefulness of this new approach to causal inference in economics and business. I’d like to pick up one of those points here, as this is an argument I frequently hear when talking to people with a classical econometrics training. Continue reading No Free Lunch in Causal Inference

Causality for Policy Assessment and Impact Analysis

Here is a great introductory lecture into causal inference and the power of directed acyclic graphs / bayesian networks. It repeats a point I made earlier on this blog that big data alone, without a causal model (i.e., theory) to support it, is simply not sufficient for making causal claims. Continue reading Causality for Policy Assessment and Impact Analysis