The causal-inference tag has no wiki summary.
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Online resources for philosophy of causation for causal inference
Can you recommend any books, articles, essays, online tutorials/courses, etc that would be interesting and useful for an epidemiologist/biostatistician to learn about the philosophy of ...
2
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1answer
38 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!
0
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1answer
38 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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0answers
35 views
Econometrics: Sargan test
Here are 3 questions about econometrics and R codes.
Test the endogeneity of the variable EDUC:
...
1
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1answer
49 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?
...
0
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1answer
25 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).
...
2
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0answers
21 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 ...
3
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1answer
30 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 ...
3
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0answers
50 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 ...
0
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1answer
52 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 ...
1
vote
3answers
77 views
What is endogeneity and what does it mean substantively? As an extension what is exogeneity?
My apologies if this is an obtuse question, I am neither a statistician nor a econometrician but as a student is empirical methods this question plagues me.
I understand that $$X'\epsilon=0$$ not ...
2
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0answers
32 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 ...
2
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1answer
18 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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0answers
42 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 ...
2
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1answer
58 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 ...
0
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1answer
20 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 ...
2
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2answers
140 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 ...
3
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1answer
84 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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2answers
92 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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0answers
208 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 ...
5
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3answers
240 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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1answer
106 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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1answer
276 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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1answer
108 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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1answer
175 views
What if only control variables are significant in a differences-in-differences analysis?
Regarding the standard DID model:
$$
y=\alpha+\beta_1\text{treat}+\beta_2\text{post}+\beta_3\text{treatā
post}+u
$$
What exactly does it mean if say $\beta_3$ is not statistically significant, but ...
12
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2answers
217 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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2answers
70 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 ...
3
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2answers
112 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 ...
3
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3answers
262 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 ...
1
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1answer
113 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 ...
4
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2answers
146 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 ...
0
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2answers
691 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 ...
3
votes
2answers
267 views
Effect of one independent variable of several dependent variables ā 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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1answer
291 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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1answer
166 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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3answers
403 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 ...
3
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2answers
195 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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3answers
210 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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0answers
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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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0answers
90 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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2answers
198 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 donĀ“t know how to add clarifications. My ...
0
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1answer
178 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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0answers
186 views
Estimating treatment on treated (TOT) effect
Suppose I am interested in the effects of maternal smoking during pregnancy on infant birth weight.
However, instead of data on maternal smoking during pregnancy, I have a natural experiment for ...
6
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4answers
859 views
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 ...
5
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3answers
2k views
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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2answers
109 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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2answers
260 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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0answers
181 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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2answers
359 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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3answers
162 views
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 ...
