As a beginning graduate student in the social sciences, what sort of software should you use to
do your work? More importantly, what principles should guide your choices? This article offers
some answers. The short version is: write using a good text editor (there are several to choose
from); analyze quantitative data with R or Stata; minimize errors by storing your work in a simple
format (plain text is best) and documenting it properly. Keep your projects in a version control
system. Back everything up regularly and automatically. Don’t get bogged down by gadgets, utilities or other accoutrements: they are there to help you do your work, but often waste your time
by tempting you to tweak, update and generally futz with them. To help you get started, I provide
a short discussion of the Emacs Starter Kit for the Social Sciences, a drop-in set of useful defaults
designed to help you get started using Emacs (a powerful, free text-editor) for data analysis and
writing.

Reversible Markov Chains and Random Walks on Graphs

This book is aimed at senior undergraduates and graduate students in Engi-
neering, Science, Mathematics, and Computing. It expects familiarity with
calculus, probability theory, and linear algebra as taught in a rst- or second-
year undergraduate course on mathematics for scientists and engineers.
Conventional courses on information theory cover not only the beauti-
ful theoretical ideas of Shannon, but also practical solutions to communica-
tion problems. This book goes further, bringing in Bayesian data modelling,
Monte Carlo methods, variational methods, clustering algorithms, and neural
networks.
Why unify information theory and machine learning? Because they are
two sides of the same coin. In the 1960s, a single eld, cybernetics, was
populated by information theorists, computer scientists, and neuroscientists,
all studying common problems. Information theory and machine learning still
belong together. Brains are the ultimate compression and communication
systems. And the state-of-the-art algorithms for both data compression and
error-correcting codes use the same tools as machine learning.

Abstract: This review presents empirical researchers with recent advances
in causal inference, and stresses the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of
multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and
the methods that have been developed for the assessment of such claims.
These advances are illustrated using a general theory of causation based
on the Structural Causal Model (SCM) described in Pearl (2000a), which
subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals.
In particular, the paper surveys the development of mathematical tools for
inferring (from a combination of data and assumptions) answers to three
types of causal queries: (1) queries about the effects of potential interventions, (also called “causal effects” or “policy evaluation”) (2) queries about
probabilities of counterfactuals, (including assessment of “regret,” “attribution” or “causes of effects”) and (3) queries about direct and indirect
effects (also known as “mediation”). Finally, the paper defines the formal
and conceptual relationships between the structural and potential-outcome
frameworks and presents tools for a symbiotic analysis that uses the strong
features of both.
Keywords and phrases: Structuralequation models, confounding,graphical methods, counterfactuals, causal effects, potential-outcome, mediation,
policy evaluation, causes of effects

This cookbook emerged as small collection of formulae while I was taking statistics courses at UC Berkeley, but quickly developed into a comprehensive summary of various topics in basic probability theory and statistics.