Tagged Questions

The relationship between cause and effect.

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Unbiased estimate for treatment effect in block randomized experiment where probability of treatment varies by block

I want to analyze the results of a block randomized experiment. Within each block, units are randomized to treatment and control as if that block were a completely randomized experiment. Specifically, ...
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2 Questions on R Package CausalImpact

I have three related questions on the package CausalImpact in R. The package can be found here and a reproducible example is below. Do I basically understand correctly, that the model makes "1-step ...
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How exactly does one derive the efficient influence curve?

I've been studying targeted maximum likelihood and I come from a CS background. I am pretty lost. Can someone explain, with an example if possible, how exactly one derives the efficient influence ...
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What are key papers discussing causal inference from a missing data perspective?

The Rubin Causal Model (RCM), also called Potential Outcome Framework, assumes any unit in a population has potential outcomes under any treatment relevant in a study. For example $Y_1$ denotes the ...
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SUTVA vs. independence

In the Wikipedia article on the Rubin causal model I stumbled upon the following quote: We require that "the [potential outcome] observation on one unit should be unaffected by the particular ...
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Does no correlation imply no causality?

I know that correlation does not imply causality but does an absence of correlation imply absence of causality?
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Forgive the layperson nature of this question, however I recently stumbled across a blog post showing an example application of Granger casualty, and before I leap too deep into learning more and ...
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How can I compute the standard error of the Wald estimator?

According to Cameron and Trivedi Microeconometrics 2006, page 98-99, the Wald estimator can be written : $$\widehat{\beta}_{Wald} = \frac{(\bar{y_1} - \bar{y_0})}{(\bar{x_1} - \bar{x_0})}$$ with :...
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Describing the causal effect of coffe and heat on esophageal tumor. Interaction, confounding, mediation?

The public health world is discussing in these days the news that coffee is no longer considered carcinogenic but heat of the drinks is the real culprit. I'm trying to visualizing this as a causal ...
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Linear regression and causality in a randomized controlled experiment

We know that a linear regression Y on X doesn't imply causation X->Y. It just means that Y is dependant on X in this model. For example, in a general case, I cannot simply run a regression of Test ...
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showing causality between police killings and demographics (i.e. race, class, gender, location)

I've got a whole lot of police data and was wondering what sort of approaches I could use to show strong correlation, and if possible, causal effects, between police killings/arrest & call-ins for ...
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Finding Markov Blanket for a dataset in R or Python [closed]

I want to find Markov blanket for a dataset in R or python. The cause- effect relationship for the dataset has not been given. Is there way in R or python such that I can input my dataset and get as ...
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Lagged time series analysis

Given 3 continuous, normally distributed, independent, time-series vectors (X, Y, Z) and 1 continuous, Poisson distributed, dependant, time-series vector (A). How can I rank the independent vectors in ...
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Control variables in instrumental variable regression

I have seen published papers include "exogenous controls" in their instrumental variables regression. Can someone explain: What is meant by "exogenous" in this case? The purpose of these "controls" ...
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Causal Graph using Bayesian Network

I am currently doing a project in which the dataset is a lung cancer dataset. There is a training file which consists of 7 unnamed parameters (Attributes) and each of them have around 1000 values ...
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Causal Inference from cross-sectional survey data with recall questions and associated problems

I have a dataset from a cross-sectional study (n=121) where people where asked about production characteristics in 2015 and how they recall their production in 2010. One set of example questions could ...
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What does “complete i” subscript mean in Gelman's model of incumbency in congressional elections?

I'm having a look at section 14.3, "Regression for causal inference: incumbency in congressional elections" in Gelman et al's Bayesian Data Analysis, third edition, pages 358-362. I'm looking for the ...
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Estimate causal effect with lots of missing values

This is a generic question. Suppose you run an experiment with units randomized between a treatment and a control group. Imagine the response rate is very low in each group, say, below 10% and with a ...
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How to inferring the casual relation between two point processes? Edit

I have two point processes. One is the spike time of a neuron and the other is when a rat licks the sugar water. I want to find out if there is any correlation and causation between these two ...
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Find relationship in two time series datasets

I have two datasets: Ingested ingredients at a point in time e.g. | 24/04/2016 11:56:33 | Tomatoe | | 24/04/2016 11:56:33 | White rice | | 24/04/2016 14:34:01 | Mars Bar | Symptoms | 24/...
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Causal Impact and using multiple control series with their regressors

Hi all I am analyzing several DMA's for campaign effectiveness using the CausalImpact package by Kay Brodersen. I have data for participants and non-participants INCLUDING their contemporaneous ...
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What is the relationship between LATE and TOT?

My understanding of LATE was that it was the effect of a treatment on individuals who were induced to be treated by the experiment. That is, the effect on compliers. My understanding of Treatment-on-...
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do(x) operator meaning?

I have seen the $do(x)$ operator everywhere in some literature review I am doing on Causality (see, for instance this wikipedia entry). However, I cannot find a formal and general definition of this ...
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K-fold cross validation error in CausalImpact

I want to know how to get the prediction error in CausalImpact()function of CausalImpact R package. I am looking for something like 10 fold cross validation error?
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Matching data before regression (multiple treatment variables)

I have the dataset for the health of patients along with various treatments they were given. In a normal case, I would just use linear regression to fit a model [y ~ t1 + t2 + t3 ... +tn]. This will ...
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Interpretation of CausalImpact package output

I have a question on how to interpret the predictors used in building the model. As per this link https://google.github.io/CausalImpact/CausalImpact.html I used the following code plot(impact$model$...
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Control group construction in Synthetic Control Method

I want to use the Synthetic Control Method to estimate the effect of the adaption of a new ballot institution (treatment) on fiscal policy. My sample consists of 20 units observed at 100 time points. ...
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How to deal with heterogeneous data set

Problem I have a dataset that contains three types of objects: simple objects, object groups and meta-groups. Meta-groups contain simple objects and groups. Each simple object correspond to an ...
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Controlling for a variable highly correlated with the variable of interest

I want to see if there's a relationship between $x$ and $y$. A variable $z$ is highly (but not perfectly) correlated with $x$. I want to check that $z$ is only related $y$ through $x$, and not ...
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What is the difference between correlation, causation and prediction?

Suppose we have a set of events $\Omega$, containing events $A$ and $B$. My econometrics professor tried to distinguish the following three terms today. Causation --- $A$ causes $B$ if the ...
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Causal and conditional probability

I was trying to understand causal probabilities better by reading this article http://lesswrong.com/lw/ev3/causal_diagrams_and_causal_models/ I was interested in the last example where we have 3 ...
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Why does propensity score matching work for causal inference?

Propensity score matching is used for make causal inferences in observational studies (see the Rosenbaum / Rubin paper). What's the simple intuition behind why it works? In other words, why if we ...
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CausalImpact and choosing the start of effect time-frame

Is it probable, to experimentally choose a prior starting point to the factual starting point of a n effect in order to validate the package's results? I guess the point gets more clear if you look ...
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How does Causalimpact work? (please see more specific questions in the description)

How does CausalImpact behave when the number of data points in the time-series is unequal to n times the set length of a season (for example when there are 30 data points with the length of the season ...
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Extracting influence counts from Model variables or data

To idetifying the important activity performed from users who have been converted in last N days. So, I have tried GLM, Rpart and Random forest models which can give me the impoprtant activities (in ...
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In a causal modeling framework, we are concerned with measuring an association between an exposure and an outcome. To do that, we usually fit a regression model for the outcome as the "y" on the ...
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Coalitional effect in logistic regression and assessing explanarory variable contribution

I have a problem that could be described as logistic regression with all dichotomous variables: 1 response variable (DV) Y (I would call it later as a feature/violet star) and 5 explanatory variables (...
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In this question I would like to ask you to choose between two simple scenarios of testing differences of rates between two random variable of Poisson distribution over different time periods. We ...
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Granger causality test vs my own custom causality test

I have a panel data regression with fixed effects, but for simplicity it is such that a lagged explanatory variable is significantly correlated with the dependent variable, which I believe is ...
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Marketing/Sales Mix/Response Models: approaches and comparisons

CV/SO Community: I am probably skirting (or crossing) the line of the preference for questions that can be answered vs. those that can (only) be discussed. That said, I'm trying to wrap my head ...
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Multiple intervention causal inference

I am currently trying to implement a causal inference in my graph (DAG). What I have is the structure of the graph and 3 groups of multiple intervention data. So, for simple example : The structure ...
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Coefficient of Quasi-Randomized Control Trial with different Treatment Intensity

I need some thoughts on the following problem which I have not been able to solve. I have thought of using different methods including OLS, FE, and PSM but I am not sure what to use. Here is the ...
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Can external cause ever be discounted?

We are often advised not to confuse correlation with causation. Fortunately techniques do exist to assess the likelihood that an outcome does indeed result from a cause. 1 2 But in some cases it is ...
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causal impact - adding multiple control groups

I want to run an analysis using causal impact tool. I have one test group but multiple control groups. Can I use multiple control groups all together in one model? Eg: Y = test and A,B,C as control ...
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What does the error “pre.period must span at least 3 time points” in the CausalImpact R package mean?

I've been encountering the error "pre.period must span at least 3 time points" when using the package. Can someone help me understand why the package requires me to have at least 3 time points and ...
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What are the pros and cons of employing LASSO for causality analysis?

It looks like social sciences are impressed by Statistical Learning and its results. A couple of months ago, I heard Imbens saying: "LASSO is the new OLS". My problem with this is that I've been ...
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Figuring out causation from event data?

Suppose I have event data showing these things: users who signed up users who signed up and then went on to do main important activity users who signed up and then went on to invite a coworker ...
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When does the Rubin Causal Model fail in practice?

The Rubin Causal Model frames the causal inference question as the problem of inferring missing potential outcomes (what the outcome would have been if a unit had received a different treatment) in ...