Causal inference tries to quantify the effect of a change in $X$ on $Y$ whilst holding constant or eliminating all other relevant factors which might influence this relationship.

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Finding the corresponding bayesian network of a predefined joint probability distribution

Given a joint probability distribution over the variables $X_1,X_2,\dots,X_n$. Is there an algorithm for constructing the corresponding Bayesian Network?
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65 views

Are latent variable models modelling causality?

Is the purpose of latent variable models to model causality, where the causes are not observable i.e. latent? Are latent variables modelling causes of the observable variables? Thanks and regards!
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42 views

Terminology to Use

Should you say a factor is associated with a disease or disease risk? For example, which is better to say: Smoking is associated with lung cancer Smoking is associated with lung cancer risk
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484 views

Econometrics: Sargan test

Here are 3 questions about econometrics and R codes. Test the endogeneity of the variable EDUC: ...
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278 views

What test is this for endogenous variables?

Can somebody tell me whether the following R code (for econometrics endogenous variables) is for a Hausman test, a Nakamura test, or some other test? ...
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470 views

What does plausibility or plausibile mechanism mean?

In Bradford Hill criteria for causality Plausibility: A plausible mechanism between cause and effect is helpful (but Hill noted that knowledge of the mechanism is limited by current knowledge). ...
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130 views

Choice of path weights in SEM conceptual model using openMx

I am reviewing the R package OpenMx for a genetic epidemiology analysis in order to learn how to specify and fit SEM models. I am new to this so bear with me. I am following the example on page 59 of ...
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76 views

Multiple imputation for variables used to calculate regression weights

My basic question: is there anything that you can't impute using MI? My more complicated question: Consider the regression $Y=\rho T+X'\beta+\epsilon$. For whatever reason, you want to weight the ...
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164 views

Properties of bivariate standard normal and implied conditional probability in the Roy model

Sorry for the long title, but my problem is quite specific and hard to explain in one title. I am currently learning about the Roy Model (treatment effect analysis). There is one derivation step at ...
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102 views

How to quantify mis-specification bias and compare against smoothing bias for a non-parametric estimate of a randomly allocated continuous treatment?

Suppose that there is a data-generating process $$ y = \alpha + g(x) + \epsilon $$ which is to say that an outcome is some function of $x$. Suppose that $x$ is randomly assigned, so ...
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What do “endogeneity” and “exogeneity” mean substantively?

I understand that the basic definition of endogeneity is that $$ X'\epsilon=0 $$ is not satisfied, but what does this mean in a real world sense? I read the Wikipedia article, with the supply and ...
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77 views

Causal identification and penalized splines

I just got a rejection from an economics journal. Among the reasons cited for rejection were: the benefits of using the semi-parametric method are not clearly brought out compared to ...
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26 views

Choice of referent twin in twin difference model

Carlin (2005) points out that mixed effects models specifically for twin data can be simplified by calculating differences between paired clusters. This allows for modeling specifically the within ...
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68 views

Counterfactuals for Variables with Negative Values

Lets imagine I have estimated the following simple linear regression model: $y_{i} = 10 + 0.5x_{i} + \varepsilon_{i} $, and want to work out the counter-factual, or what would $ y_{i}$ be in the ...
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369 views

Relation between causal inference and prediction (classification and regression)

I was wondering what relation and differences are between causal inference and prediction (classification and regression)? For example, In prediction, we have predictor/input variables and ...
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1answer
69 views

Relative efficiency of matching versus adjustment to handle confounding effects in highly disproportionate populations

I am reviewing a paper where the authors compare cancer outcomes (binary) between two groups, one having a small sample size of 200 and the other having over 55,000. The authors then claim that, due ...
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2answers
206 views

Causality, omitted variable bias

This might be a basic question, but I want to be sure that what I'm doing is right. I have a model that suggests that variable X causes both Y and Z. When I regress Y on X, or Z on X, I get positive ...
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93 views

I have GBs of Event-Based Data. How do I figure out causation?

I have a lot of event-based data about users of our website. For example, data in the format (verb, timestamp). There's about 10 or so different verbs (call them A, B, C, etc). I'm interested in ...
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395 views

Do we need Overlap/Common Support in case of a parametric regression?

If I want to make a causal statement based on selection on observables. One typically assumes "Common Support" (/"Overlap") - which means that for any value of the confounding variables X a unit i can ...
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428 views

An alternative to “Granger Causality” test when (short) time series are not stationary?

I have two short time series (x and y), and I wish to find out if x "effects" (is correlated with) y. Obviously, since the two are time series, using a simple correlation is the wrong way to go. I ...
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453 views

Big Data vs multiple hypothesis testing?

Nate Silver in his excellent "The Noise and the Signal" warned that we are much in awe of Big Data. But, that Big Data predictions in many fields have been disastrous (financial markets and economics ...
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223 views

Causality test for logistic regression

For time series there is the Granger causality test. Is there some causality test for the logistic regression?
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821 views

Formula for one-sided Hodrick-Prescott filter

I am not very familiar with filters. The Hodrick-Prescott filter as one can find it e.g. in wikipedia is two-sided. I also found an R implementation for this in the R package mFilter. There the filter ...
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121 views

Why arrange variables by causality in bivariate regression?

Suppose we have variables $(X,Y)$ and we have theory tell us that $X$ $\overset{\text{cause}}{\implies} Y$. Perhaps they're time-series variables and it would be common to see something like this: ...
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412 views

What if only control variables are significant in a differences-in-differences analysis?

Regarding the standard DID model: $$ y=\alpha+\beta_1\text{treat}+\beta_2\text{post}+\beta_3\text{treat⋅post}+u $$ What exactly does it mean if say $\beta_3$ is not statistically significant, but ...
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276 views

Introduction to causal analysis

What are good books that introduce causal analysis? I'm thinking of an introduction that both explains the principles of causal analysis and shows how different statistical methods could be used to ...
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86 views

Causal stats with one event and multiple time series?

I've worked with certain causal/predictive techniques when handling two time series, but this problem is different from what I am used to and I'm not sure how to proceed. I would like to see the ...
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348 views

What test should I use to determine if a policy change had a statistically significant impact on website registrations?

A client's website was operating under a certain policy for membership sign ups for over a year. At the start of October 2012 the client implemented a new policy for sign ups that was supposed to ...
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498 views

Fuzzy regression discontinuity design and exclusion restriction

In a fuzzy regression discontinuity design, what does the exclusion restriction look like in terms of a conditional expectation between the instrument in the first stage and the error term in the ...
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1answer
205 views

How to account for a regressand affecting a regressor?

I forget the terminology, but this happens when you regress, say, $Y$ on a list of variables, and you suspect that $Y$ affects, say, $x_3$ in addition to $x_3$ affecting $Y.$ I forget how this is ...
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187 views

Causation implication

I recently read an article about how you can increase longevity by sleeping less. This article, like many others I've read, references a statistical study and implies that causation was found between ...
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2k views

Proving Causality with t-test/regression

Earlier today I was discussing statistical analysis software with a colleague of mine. My colleague had primarily used SPSS in previous work for performing t-tests, anovas, manovas, and other ...
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518 views

Effect of one independent variable of several dependent variables – best strategy?

I have a question regarding which analysis strategy is best suited for our objective. In an exploratory study based on data from a survey we conducted ourselves in India, we are analyzing the ...
3
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597 views

Heckman selection model with difference-in-differences specification

Following my question on Tobit with DiD specification I am wondering if it is possible to estimate a heckman sample selection model with a Difference in Differences specification? For example in ...
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525 views

Is it possible to have a variable that acts as both an effect modifier and a confounder?

Is it possible to have a variable that acts as both an effect (measurement) modifier and a confounder for a given pair of risk-outcome associations? I'm still a little unsure of the distinction. I've ...
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623 views

Random assignment: why bother?

Random assignment is valuable because it ensures independence of treatment from potential outcomes. That is how it leads to unbiased estimates of the average treatment effect. But other assignment ...
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364 views

How to avoid the problem of two-way causality?

I am studying the effect of social capital on households' income. I am doing multiple regression to estimate this effect. For this, I have households' income as dependent variable and social capital ...
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252 views

Formal definition of random assignment

I am looking for a formal definition of random assignment. Let $\mathbf{Z}$ be a vector of treatment assignments in which each element is 0 (unit not assigned to treatment) or 1 (unit assigned to ...
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311 views

Implication / Interpretation of long term equilibrium VECM

I want to test the influence of exchange rates on a price index and struggle with the interpretations. My variables are I(1) First, I ran an OLS on first differenced variables which indicated a ...
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108 views

Difference in Differences: id switches between treatment and control group

In my difference in differences model firms $> x$ belong to the treatment group whereas firms$< x$ act as control. I have a two period model: In $t_1$ firm $i$ is $> x$ and thus ...
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291 views

How do I estimate a differences in differences model when the dependent variable has many zeros?

Is there any way to run an OLS difference in differences model when the dependent variable (investment) has lots of observations which are truly zero? I don´t know how to add clarifications. My ...
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279 views

Clustered standard errors in 2-period Dif-in-Dif?

in order to rectify invalid t-stats because of autocorrelation in Difference-in-Differences (DnD) models, Duflo et al (2004) propose (among other solutions) to collapse data so as to have a ...
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Why use control variables in differences-in-differences?

I have a question on the differences-in-differences approach with the following standard equation: $$ y= a + b_1\text{treat}+ b_2\text{post} + b_3\text{treat}\cdot\text{post} + u $$ where treat is a ...
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X and Y are not correlated, but X is significant predictor of Y in multiple regression. What does it mean?

X and Y are not correlated (-.01); however, when I place X in a multiple regression predicting Y, alongside three (A, B, C) other (related) variables, X and two other variables (A, B) are significant ...
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166 views

Must there be “an effect to be mediated” in mediational analysis (i.e., must IVs & DVs be correlated)?

Baron and Kenny outlined several steps to aid in determining if a mediational analysis is appropriate to test a particular hypothesis. The very first step was "Show that the initial [independent] ...
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702 views

How to control for industry effects in regression?

Right now I'm working on an analysis of influence of cultural aspects on investment mode preference. However I have to control for many other factors, for example industry, since some industries, for ...
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337 views

Inductive vs deductive Inference

I am curious to know exactly, what are the (possible) differences between inductive and deductive statistical inferences in applied statistics. Suggestions for some good resources to learn their ...
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580 views

Differentiating correlation and causation using conditional probablity

I'm trying to understand the difference between causation and correlation using conditional probabilities. From what I understand, one may quantify causation by $P(E_1|E_2) / P(E_1)$. For example, ...
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How do you find causal relationships in data?

Lets say I have a table with columns "A", "B" Is there a statistical method to determine if "A" causes "B" to happen? One can't really use Pearson's r, because: it only tests the correlation ...
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What to conclude when you fail to find an association in an epidemiological study?

Normally when somebody finds an association in an epidemiological study people are quick to point out that it doesn't prove causality, that there are problems of missing co-founders, that it is at ...